William McKnight

Database replication benchmark: Airbyte vs. Fivetran
McKnight Consulting Group made a deep benchmark analysis on database replication for Postgres, comparing Airbyte and Fivetran. The analysis focused on 3 elements: performances, costs, and ease of us. Discover which solution is the best in the market for database replication.

Unlock the full potential of your data with cloud-native, AI-powered master data management. Our innovative data management solutions not only streamline data processes but also deliver new insights, driving your business forward with confidence and precision.
Discover the future of data in this comprehensive report, where advanced analytics meet unparalleled accuracy.

Bytewax features a modern architecture that combines the performance of a Rust engine for distributed, parallel streaming with the ease of use of Python. The outcome is a stateful stream processor that rivals the functionality and performance of traditional Java-based tools like Flink, without any of the drawbacks. Enable all your Python teams to work with streaming!

Enterprise data integration platforms have ascended to new levels of performance, harnessing artificial intelligence (AI), generative AI (GenAI), machine learning (ML), workflow automation and cloud-native capabilities.
Conventional Extract, Transform, Load (ETL) processes, once cumbersome and time-consuming, have evolved with agile methods that speed development — allowing for changes within minutes rather than months.
Explore the ever-advancing data integration landscape in the… Show more

Enterprise data integration platforms have ascended to new levels of performance, harnessing artificial intelligence (AI), generative AI (GenAI), machine learning (ML), workflow automation and cloud-native capabilities.
Conventional Extract, Transform, Load (ETL) processes, once cumbersome and time-consuming, have evolved with agile methods that speed development — allowing for changes within minutes rather than months.
Explore the ever-advancing data integration landscape in the “Enterprise Data Integration Contribution Ranking Report” by McKnight Consulting Group. Show less

McKnight Consulting Group conducted an industry standard TPC Benchmark™ H (TPC-H) study which revealed that Actian Data Platform delivers significantly better performance than Databricks, Snowflake and BigQuery with Snowflake proving 8x more expensive than Actian Data Platform.
These benchmark results highlight what our customers have already known: Actian is a leader in data platforms, helping organizations lower TCO while maintaining high analytical performance.

In this study, we sought to compare the total cost of ownership between Fivetran HVR and Qlik Replicate, based on similar levels of operational latency.

In this Benchmark report, we introduce SAP Datasphere. We compare the task of creating, integrating, distributing, and managing a data fabric with a common DIY set to SAP. Looking across all organization sizes, we found that a DIY data fabric deployment over three years cost 2.4x more than SAP Datasphere. These sharp cost advantages played out across all aspects of adoption—data fabric infrastructure, initial migration/build, CI/CD, and administration.

The benchmark aims to demonstrate the performance of DataStax Astra DB Serverless (Vector) compared to the Pinecone vector database within the burgeoning vector search/database sector. This report contains comprehensive detail on our benchmark and an analysis of the results.

Diamanti Provides Fastest SQL Server Performance at the Lowest TCO
Diamanti’s customers have voiced their need for a solution to best deploy containerized Microsoft SQL Server across hybrid cloud. As part of this effort, we’ve commissioned the McKnight Group, an independent consulting firm, to conduct an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited… Show more

Diamanti Provides Fastest SQL Server Performance at the Lowest TCO
Diamanti’s customers have voiced their need for a solution to best deploy containerized Microsoft SQL Server across hybrid cloud. As part of this effort, we’ve commissioned the McKnight Group, an independent consulting firm, to conduct an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements. Show less

The field study examines the process of migrating .NET applications and data from on-premises deployment to both Microsoft Azure and Amazon Web Service (AWS) PaaS offerings. Our testing showed that both cloud providers have a strong value proposition for migrating on-premises workloads. Companies can take advantage of Azure Hybrid Benefit, a licensing offer that helps you save up to 85% of the overall cost of SQL workloads in the cloud. This can fundamentally alter the economics behind a data-… Show more

The field study examines the process of migrating .NET applications and data from on-premises deployment to both Microsoft Azure and Amazon Web Service (AWS) PaaS offerings. Our testing showed that both cloud providers have a strong value proposition for migrating on-premises workloads. Companies can take advantage of Azure Hybrid Benefit, a licensing offer that helps you save up to 85% of the overall cost of SQL workloads in the cloud. This can fundamentally alter the economics behind a data- or transaction-based business model. Show less

We evaluated two leading iPaaS tools—Workato and Boomi—specifically across the top tasks a business would perform with the tools for governance and data security. Both Workato and Boomi offer robust features in these areas. Workato provides a user-friendly interface that allows businesses to easily manage user permissions and access controls, ensuring that sensitive data is only accessible to authorized individuals. Additionally, Workato offers advanced security measures such as encryption and… Show more

We evaluated two leading iPaaS tools—Workato and Boomi—specifically across the top tasks a business would perform with the tools for governance and data security. Both Workato and Boomi offer robust features in these areas. Workato provides a user-friendly interface that allows businesses to easily manage user permissions and access controls, ensuring that sensitive data is only accessible to authorized individuals. Additionally, Workato offers advanced security measures such as encryption and multi-factor authentication to protect data from unauthorized access. On the other hand, Boomi provides a comprehensive set of tools for data governance and compliance, allowing businesses to define and enforce data security policies across their organization. Show less

The increasingly complex landscape of data management for enterprises has brought to light the need for tools to manage diverse platforms and provide overall management. These tools focus on efficient test data management; integration, lifecycle management, security, compliance, and storage optimization. We determined that for the equivalent usage (consisting of typical deployment, provisioning, and masking activities), IBM Optim would result in labor and infrastructure costs of $109,300… Show more

The increasingly complex landscape of data management for enterprises has brought to light the need for tools to manage diverse platforms and provide overall management. These tools focus on efficient test data management; integration, lifecycle management, security, compliance, and storage optimization. We determined that for the equivalent usage (consisting of typical deployment, provisioning, and masking activities), IBM Optim would result in labor and infrastructure costs of $109,300 compared to $75,164 for Delphix over the first year (one setup), a difference of 31%. Show less

This report focuses on the latency of two top bare-metal server providers: Lumen Edge Cloud Services (Lumen) and Amazon Web Services (AWS). For our testing, we defined a high-performance model, mixing applications that can process 1,000 transactions per second and demand a maximum latency of 5ms or less, while the NGINX reverse proxy handles HTTP requests.

We performed a field test to assess how Snowplow compares to GA4 for a retail organization that realizes the mandate for digital analytics. To make the test as fair as possible, we used both Google Analytics and Snowplow in vanilla e-commerce implementations. This means that for both solutions, we used the out-of-the-box e-commerce events.

This benchmark report provides insight into how well Zoom and Jitsi supports enterprise applications requiring real-time customer engagement. We capture time to value by assessing the steps involved in initial setup, scaled deployment, and production integration, and we assess TCO by evaluating both software and labor costs associated with a deployment. The benchmark can also be used to determine the implementation steps for each solution.

You should never have to sacrifice budget control to improve the performance of your analytics database.

Exasol’s no-compromise analytics database takes care of this for you, delivering significant productivity gains, cost-savings and flexibility, without any trade-offs. Don’t just take our word for it, read the latest report conducted by McKnight Consulting Group, on Cloud Analytics Database Performance testing against Exasol, Snowflake, and another major cloud data warehouse (April… Show more

You should never have to sacrifice budget control to improve the performance of your analytics database.

Exasol’s no-compromise analytics database takes care of this for you, delivering significant productivity gains, cost-savings and flexibility, without any trade-offs. Don’t just take our word for it, read the latest report conducted by McKnight Consulting Group, on Cloud Analytics Database Performance testing against Exasol, Snowflake, and another major cloud data warehouse (April 2023).

The report proves that Exasol wins on:

Query response times:
3.6x faster than Snowflake
20x faster than the unnamed data warehouse
Price-performance:
Snowflake is nearly 17x more expensive than Exasol
Unnamed data warehouse is over 20x more expensive Show less

This report outlines the results from two Field Tests (one transactional and the other analytic) derived from the industry-standard TPC Benchmark™ E (TPC-E) and TPC Benchmark™ H (TPC-H). The tests compare two IaaS cloud database offerings running Red Hat Enterprise Linux (RHEL), configured as follows:

RHEL 8.6 with Microsoft SQL Server 2022 Enterprise on r6idn.8xlarge Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances with gp3 volumes.
RHEL 8.6 with Microsoft SQL Server… Show more

This report outlines the results from two Field Tests (one transactional and the other analytic) derived from the industry-standard TPC Benchmark™ E (TPC-E) and TPC Benchmark™ H (TPC-H). The tests compare two IaaS cloud database offerings running Red Hat Enterprise Linux (RHEL), configured as follows:

RHEL 8.6 with Microsoft SQL Server 2022 Enterprise on r6idn.8xlarge Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances with gp3 volumes.
RHEL 8.6 with Microsoft SQL Server 2022 Enterprise on a E32bdsv5 Azure Virtual Machine (VM) with Premium SSD v2 disks. Show less

A total cost of ownership (TCO) comparison against several real-world, and comparable workloads for NATS and Kafka for streaming. Users want to know if the “simplicity” is a trade-off vs functionality or performance.

To evaluate distributed databases, we conducted a Transactional Field Test derived from the industry-standard TPC Benchmark™ C (TPC-C). We compared fully-managed as-a-service offerings of cloud-distributed databases:

Azure Cosmos DB for PostgreSQL
CockroachDB Dedicated
YugabyteDB Managed

In this benchmark, we set out to test the value of the marketplace.

We used the marketplace to buy and deploy three well-known products and contrasted that experience with buying each service separately. We selected a public cloud cost-saving solution provided as a service, an event streaming platform, and an application delivery solution frequently used for its load balancing and web firewall capabilities.

Microsoft Teams (Teams) is a collaboration platform that combines workplace chat, video meetings, file storage, and application integration. It is part of the Microsoft 365 suite of applications and is designed to help teams stay organized and connected. Teams allows users to communicate and collaborate in real-time, share files, and access applications from a single platform. As application performance is key to productivity, Teams has a regular cadence of releases focused on enhancements to… Show more

Microsoft Teams (Teams) is a collaboration platform that combines workplace chat, video meetings, file storage, and application integration. It is part of the Microsoft 365 suite of applications and is designed to help teams stay organized and connected. Teams allows users to communicate and collaborate in real-time, share files, and access applications from a single platform. As application performance is key to productivity, Teams has a regular cadence of releases focused on enhancements to functionality, performance, and ease of use.

This report benchmarks the performance differences between two versions of the Microsoft Teams desktop application for Windows: classic Teams (version 1.1, release June 2022) and new Teams (version 2.1, preview release March 2023). It showcases the improvements provided by new Teams by comparing the results of tests across three domains—installation behavior, application responsiveness, and resource utilization. Show less

We tested four SIEM products in this report: Micro Focus ArcSight, Splunk Enterprise Security, IBM QRadar, and Microsoft Sentinel. Micro Focus ArcSight and Splunk Enterprise Security both excelled in detecting and logging the battery of attacks, each scoring 10 out of 10 in our series. IBM QRadar failed to catch many of the attacks in our tests and fell short of Micro Focus and Splunk in the quality of results presentation. Finally, we included in our evaluation Microsoft Sentinel, which at the… Show more

We tested four SIEM products in this report: Micro Focus ArcSight, Splunk Enterprise Security, IBM QRadar, and Microsoft Sentinel. Micro Focus ArcSight and Splunk Enterprise Security both excelled in detecting and logging the battery of attacks, each scoring 10 out of 10 in our series. IBM QRadar failed to catch many of the attacks in our tests and fell short of Micro Focus and Splunk in the quality of results presentation. Finally, we included in our evaluation Microsoft Sentinel, which at the time of this testing was equipped with a pre-release implementation of the MITRE ATT&CK framework. While we provide a hands-on assessment of the Sentinel product in this report, the tool did not produce usable results in our detection tests and therefore was not included in that portion of our evaluation. Show less

This report outlines the results from two Field Tests (one transactional and the other analytic) derived from the industry-standard TPC Benchmark™ E (TPC-E) and TPC Benchmark™ H (TPC-H) to compare two IaaS cloud database offerings:

Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances.
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM).

With big data, it’s too expensive and time-consuming to move your data, so you want to bring your code to the data instead. That’s one dimension to consider beyond just the typical stored procedure language of each database. You should be able to run C, C++, Rust, GoLang, Python, and others. Consider database providers that leverage WebAssembly (Wasm), an open technology from the Cloud Native Computing Foundation, to more easily, safely, and efficiently extend the computational capabilities of… Show more

With big data, it’s too expensive and time-consuming to move your data, so you want to bring your code to the data instead. That’s one dimension to consider beyond just the typical stored procedure language of each database. You should be able to run C, C++, Rust, GoLang, Python, and others. Consider database providers that leverage WebAssembly (Wasm), an open technology from the Cloud Native Computing Foundation, to more easily, safely, and efficiently extend the computational capabilities of their offerings. Show less

Competitive markets demand rapid, well-informed decision-making to succeed. In response, enterprises are building fast and scalable data infrastructures to fuel time-sensitive decisions, provide rich customer experiences enable better business efficiencies, and gain a competitive edge.

Data security has become an undeniable part of the technology stack for modern applications. No longer an afterthought, protecting application assets—namely data—against cybercriminal activities, insider threats, and basic human negligence needs to happen early and often during the application development cycle and beyond.

Real-time observability and enterprise systems monitoring have become critical functions in information technology organizations globally. As organizations continue to digitize and automate key functions, they are introducing more complex systems, hypervisors, virtual machines, Kubernetes, devices, and applications—all of which are generating more log and event data. While the amount of usable log data is growing, there is not an attendant growth in the tools, skilled professionals, and other… Show more

Real-time observability and enterprise systems monitoring have become critical functions in information technology organizations globally. As organizations continue to digitize and automate key functions, they are introducing more complex systems, hypervisors, virtual machines, Kubernetes, devices, and applications—all of which are generating more log and event data. While the amount of usable log data is growing, there is not an attendant growth in the tools, skilled professionals, and other resources to capture, manage, and analyze this complexity. Show less

We benchmarked the usability, effort, and performance of the WEKA Data Platform against Amazon FSx for Lustre on AWS. In this hands-on benchmark, we found that WEKA provided comparable or superior usability and outperformed FSx for Lustre at similar capacities by up to 300% or more. On some of our tests, WekaFS IO latency was less than 30% that of FSx for Lustre. Our usability tests also found WEKA to be a mature and easily deployed and operated solution in AWS specifically.

An Azure Arc-enabled infrastructure is a cloud infrastructure that is managed and monitored by Microsoft Azure. It includes features such as Azure Resource Manager, Azure Monitor, and Azure Security Center. The Azure portal is a web-based management tool that provides a unified experience for managing all Azure resources. The Azure portal allows you to create, manage, and monitor Azure resources in a single, unified console. Many are managing their Microsoft Azure Arc-enabled infrastructure… Show more

An Azure Arc-enabled infrastructure is a cloud infrastructure that is managed and monitored by Microsoft Azure. It includes features such as Azure Resource Manager, Azure Monitor, and Azure Security Center. The Azure portal is a web-based management tool that provides a unified experience for managing all Azure resources. The Azure portal allows you to create, manage, and monitor Azure resources in a single, unified console. Many are managing their Microsoft Azure Arc-enabled infrastructure from an Azure portal. Show less

This report focuses on web application security mechanisms deployed in the cloud and closer to your apps. The cloud enables enterprises to rapidly differentiate and innovate with microservices and allows microservice endpoints to be cloned and scaled in a matter of minutes. It reviews F5 NGINX App Protect WAF vs. AWS WAF, Azure Web Application Firewall, and Cloudflare WAF.

https://research.gigaom.com/report/cloud-analytics-platform-total-cost-of-ownership-2/

While artificial intelligence is already changing our world, it’s really only just begun. Here’s my vision of what life might be like in 2050.

This report focuses on real-time data and how autonomous systems can be fed at scale reliably. To shed light on this challenge, we assess the ease of use of a fully managed Kafka platform—Confluent Cloud—and a self-managed open-source Apache Kafka solution.

This report focuses on the performance of cloud-enabled, enterprise-ready, popular log analytical platforms Microsoft Azure Data Explorer (part of Azure Synapse Analytics), Google BigQuery, and Snowflake. Due to cost limitations with Elasticsearch and AWS OpenSearch, we could not run our tests on Elasticsearch. Microsoft invited GigaOm to measure the performance of the Azure Data Explorer engine and compare it with its leading competitors in the log analytics space. The tests we designed intend… Show more

This report focuses on the performance of cloud-enabled, enterprise-ready, popular log analytical platforms Microsoft Azure Data Explorer (part of Azure Synapse Analytics), Google BigQuery, and Snowflake. Due to cost limitations with Elasticsearch and AWS OpenSearch, we could not run our tests on Elasticsearch. Microsoft invited GigaOm to measure the performance of the Azure Data Explorer engine and compare it with its leading competitors in the log analytics space. The tests we designed intend to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry might encounter in their log analytics.

In this report, we tested complex workloads with a volume of 100TB of data and concurrency of 1 and 50 concurrent users. The testing was conducted using comparable hardware configurations on Microsoft Azure and Google Cloud. Show less

Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight… Show more

Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices. This change allows organizations to knit together disparate systems and applications without creating technical debt from tight coupling with custom code or proprietary, unwieldy vendor tools.

This report reveals the results of performance testing we completed on these API and microservices management platforms: Kong Enterprise, Google Cloud Apigee X, and MuleSoft Anypoint Flex Gateway. Show less

This study examines the full cost and true value of self-managed OSS Apache Cassandra® vs DataStax AstraDB fully managed DBaaS in Google Cloud. Our three-year total cost of ownership (TCO) calculations account for dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on Astra DB), storage growth (each write operation adds new data) and people cost. People costs consider that certain capabilities in Astra DB needed for the workload were not available in… Show more

This study examines the full cost and true value of self-managed OSS Apache Cassandra® vs DataStax AstraDB fully managed DBaaS in Google Cloud. Our three-year total cost of ownership (TCO) calculations account for dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on Astra DB), storage growth (each write operation adds new data) and people cost. People costs consider that certain capabilities in Astra DB needed for the workload were not available in self-managed Cassandra, requiring workarounds. We used market rates and typical splits of full-time equivalent (FTE) and consulting to determine our people costs. Show less

Your Analytical Database Deployment will probably be to Multiple Clouds. Learn about the Role of the Data Warehouse in a World with Data Lakes, Data Science and Decentralization, Options for Provisioning the Data Warehouse and Why Multiple Clouds, Cloudwashing – Cloud-Enabled/Hosted vs Cloud-Native vs Cloud-Owned and Multi-Cloud Flexibility and Deployment Freedom.

Competitive data-driven organizations rely on data-intensive applications to win in the digital service economy. These applications require a robust data tier that can handle the diverse workloads demands of both transactional and analytical processing while serving an interactive, immersive customer experience. The resulting database workloads demand low-latency responses, fast streaming data ingestion, complex analytic queries, high concurrency, and large data volumes.

This report… Show more

Competitive data-driven organizations rely on data-intensive applications to win in the digital service economy. These applications require a robust data tier that can handle the diverse workloads demands of both transactional and analytical processing while serving an interactive, immersive customer experience. The resulting database workloads demand low-latency responses, fast streaming data ingestion, complex analytic queries, high concurrency, and large data volumes.

This report outlines the results from a Field Test derived from three industry standard benchmarks—TPC Benchmark™ H (TPC-H), TPC Benchmark™ DS (TPC-DS), and TPC Benchmark™ C (TPC-C)—to compare SingleStoreDB, Amazon Redshift, and Snowflake. Show less

Is a serverless DataStax Cassandra really more cost effective than managing your own Apache Cassandra clusters? We found savings, less complexity and lower infrastructure costs.

Addressing modern real-world use cases requires the application of multiple functions working together on the data. Here are some things to know to cost out the stack effectively.

The goal of our study presented in this paper is to objectively uncover whether NetApp is truly positioned to deliver on value propositions to the enterprise. To meet this objective, we designed a field test derived from monitoring, troubleshooting, optimizing, and securing scenarios common to the modern enterprise with, or in the process of, migrating to a hybrid cloud.

This test measured enterprise response to usual and important situations including greedy/degraded applications… Show more

The goal of our study presented in this paper is to objectively uncover whether NetApp is truly positioned to deliver on value propositions to the enterprise. To meet this objective, we designed a field test derived from monitoring, troubleshooting, optimizing, and securing scenarios common to the modern enterprise with, or in the process of, migrating to a hybrid cloud.

This test measured enterprise response to usual and important situations including greedy/degraded applications, underutilized infrastructure, and ransomware simulations. Show less

MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors support MLOps: the major offerings are Microsoft Azure ML, Google Vertex AI, and Amazon SageMaker. We looked at these offerings from the perspective of enterprise features and time to value.

For the analysis, we used categories of time to value and enterprise capabilities. As shown in Table 1, our assessment resulted in a score of 2.95 (out of 3) for Azure… Show more

MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors support MLOps: the major offerings are Microsoft Azure ML, Google Vertex AI, and Amazon SageMaker. We looked at these offerings from the perspective of enterprise features and time to value.

For the analysis, we used categories of time to value and enterprise capabilities. As shown in Table 1, our assessment resulted in a score of 2.95 (out of 3) for Azure ML using managed endpoints, 2.12 for Google Vertex AI, and 2.83 for Amazon SageMaker. The higher the score, the better, and the scoring rubric and methodology are detailed in an appendix to this report. Show less

The demand for data to power cloud apps, improve AI models and deliver on digital business initiatives has never been greater. Cloud MDM can help deliver the trusted business analytics and insights, faster innovation, and improved customer relationship management your company needs to stay competitive.

Here are three best practices for leveling up your organization’s use of analytics and attaining ROI with an enterprise analytics program.

This report outlines the results from two GigaOm Field Tests (one transactional and the other analytic) derived from the industry-standard TPC Benchmark™ E (TPC-E) and TPC Benchmark™ H (TPC-H) to compare two IaaS cloud database offerings:

Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances.
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM).

A report by McKnight Consulting Group used industry-standard benchmarks to test three well-known, cloud-optimized analytical platforms – Vertica in Eon Mode, Amazon Redshift, and Snowflake.

Our study examined three public cloud service offerings that use natural language processing to meet the challenge—Google Cloud Healthcare API, Amazon Comprehend Medical, and Microsoft Azure Text Analytics for Health. We manually annotated medical notes to identify terms within the documents from a common set of entities and relationships. Next, we built an annotation taxonomy by comparing the taxonomies of the three NLP solutions and created a standard mapping of the entities and relationships… Show more

Our study examined three public cloud service offerings that use natural language processing to meet the challenge—Google Cloud Healthcare API, Amazon Comprehend Medical, and Microsoft Azure Text Analytics for Health. We manually annotated medical notes to identify terms within the documents from a common set of entities and relationships. Next, we built an annotation taxonomy by comparing the taxonomies of the three NLP solutions and created a standard mapping of the entities and relationships shared by all three platforms. We then compared our annotations to the annotations of each solution, using the annotation taxonomy, and noted false negatives (not desired), true positives (desired), and false positives (not desired). Show less

Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight… Show more

Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices.

In this report, we reveal the results of performance testing we completed on two API and Microservices Management platforms: Kong Enterprise and Google Cloud Apigee X. Show less

Last year we started down the new road of uncovering enterprise analytics with machine learning. This year will see an acceleration. Here are some trends to watch.

The purpose of this Solution Spotlight is to extend the analysis from prior reports into more detail on several of the key criteria and evaluation metrics and further demonstrate Yellowbrick’s status as a Leader and potential as an Outperformer in the rapidly growing and competitive data warehouse space.

The COVID-19 pandemic and subsequent shutdowns posed a direct and unique challenge to the United Kingdom’s Department for Work and Pensions, as the number of active claimants spiked from more than 2 million just before the pandemic to more than 5 million in the span of a couple months. Learn how the DPW leveraged MongoDB to scale its microservices-based infrastructure to protect millions of citizens during a time of crisis.

This paper examines the different flavors of DWaaS to ensure you get into the right one. Then it looks at some of the key criteria that should be considered when reviewing the cloud database.

Behind the covers of the DWaaS term, there are three distinct approaches. While all include most of the benefits for DWaaS, the differences mean that the benefits will accrue quite differently according to the fit of the model to the enterprise. These are vast enough differences to actually be the… Show more

This paper examines the different flavors of DWaaS to ensure you get into the right one. Then it looks at some of the key criteria that should be considered when reviewing the cloud database.

Behind the covers of the DWaaS term, there are three distinct approaches. While all include most of the benefits for DWaaS, the differences mean that the benefits will accrue quite differently according to the fit of the model to the enterprise. These are vast enough differences to actually be the deciding factor in the DWaaS selection. Show less

Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number… Show more

Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.

To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta. Show less

Picking the wrong event streaming platform for your organization can have massive consequences in terms of fit, function and of course cost. With Apache Pulsar quickly gaining mindshare within enterprises that need a comprehensive, open source event streaming and messaging platform, the expert researchers at GigaOm decided to see how this new, up and coming technology compares to the old industry stalwart: Apache Kafka.

So how did Pulsar stack up? See for yourself.

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a selection that allows a worry-less experience with the architecture and its components.

We decided to take four… Show more

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a selection that allows a worry-less experience with the architecture and its components.

We decided to take four leading platforms – Azure, AWS, GCP and Snowflake – for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise, and for an enterprise project, matters to cost. Show less

In this paper, we focus on the higher-volume, critical-app compute and storage that is the analytic database. We have undertaken the ambitious goal of evaluating the current vendor landscape and assessing the analytic platforms that have made, or are in the process of making, the leap to a new generation of capabilities in order to support the AI-based enterprise.

For this Roadmap Report, we chose technologies powered for an enterprise-class application in a midsize to large enterprise… Show more

In this paper, we focus on the higher-volume, critical-app compute and storage that is the analytic database. We have undertaken the ambitious goal of evaluating the current vendor landscape and assessing the analytic platforms that have made, or are in the process of making, the leap to a new generation of capabilities in order to support the AI-based enterprise.

For this Roadmap Report, we chose technologies powered for an enterprise-class application in a midsize to large enterprise. We considered popularity and interest. The vendors/products chosen were:

Actian Avalanche
Amazon Redshift
Cloudera Data Platform
Google BigQuery
IBM Db2 Warehouse on Cloud and Cloud Pak for Data
Microsoft Azure Synapse Analytics
Oracle Autonomous Data Warehouse
Snowflake
Teradata Vantage
Vertica
Yellowbrick Show less

This report focuses on API management platforms deployed in the cloud. The cloud enables enterprises to differentiate and innovate with microservices at a rapid pace. It allows API endpoints to be cloned and scaled in a matter of minutes. And it offers elastic scalability compared with on-premises deployments, enabling faster server deployment and application development, and allowing less costly compute.

The economic payback of master data management starts with “build once, use often.” Master data must be accessible to each new application that is built, and these applications routinely have up to 50% effort and budget directed toward collecting master data.

This GigaOm Radar report evaluates the capabilities of notable players in the MDM space against the decision-making framework established in the Key Criteria Report for Master Data Management.

In this report, we performance tested security mechanisms on NGINX, AWS, and Azure: ModSecurity, NGINX App Protect WAF, AWS Web Application Firewall (WAF), and Azure WAF. This last product was tested as a fully managed security offering.

This study examines the full cost and true value of Cassandra self-managed on Google Cloud (GCP) and the cost of a fully managed serverless Cassandra service. We included dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on serverless Cassandra), storage growth (each write operation adds new data) and people cost in our three-year total cost of ownership calculations. People costs take into account that certain capabilities in serverless Cassandra… Show more

This study examines the full cost and true value of Cassandra self-managed on Google Cloud (GCP) and the cost of a fully managed serverless Cassandra service. We included dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on serverless Cassandra), storage growth (each write operation adds new data) and people cost in our three-year total cost of ownership calculations. People costs take into account that certain capabilities in serverless Cassandra needed for the workload were not available in self-managed Cassandra, requiring workarounds. We used market rates and typical splits of full-time equivalent (FTE) and consulting to determine our people costs. Show less

This benchmark compared Actian Zen Enterprise Server and MySQL Enterprise, both running on the same Ubuntu Linux in 8- and 16-core VMs as AWS EC2 instances, each using its ODBC driver. The benchmark used is derived from the TPC-C industry standard benchmark.

An Evaluation Guide for Technology Decision Makers.

Here are some key steps that you can take to measure and communicate the tangible return on investment for your data quality initiatives.

This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings:

Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)

MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9… Show more

MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report. Show less

The proliferation of data creates a lot of risk for many organizations, especially in healthcare organizations where privacy, timeliness, and safety are paramount. A pharmacy benefit management (PBM) company faced the risk of being overwhelmed by these demands and launched a strategic effort to build out a data platform and streaming data engine aligned around a microservices architecture. This report explores the challenges and lessons encountered in the effort.

An Evaluation Guide for Technology Decision Makers.

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources.

This report outlines the results from an analytic performance test derived from the industry-standard TPC Benchmark™ DS (TPC-DS) to compare Cloudera Data Warehouse service (CDW)—part of the broader Cloudera Data Platform (CDP)—with four prominent competitors: Amazon Redshift, Azure Synapse Analytics, Google BigQuery, and Snowflake. Overall, the test results were insightful in revealing query execution performance of these platforms.

This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings:

Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)
Both are installations of Microsoft SQL Server, and we tested Red Hat Enterprise Linux OS.

Organizations looking for enterprise data warehouses (EDWs) cannot afford to base their evaluation on query price-performance alone. There’s much more to it. You also need capabilities that reduce the time needed for configuration, management, tuning, and other tasks that can take away from valuable time spent on business analytics.

This new whitepaper from McKnight Consulting Group explores factors that can reduce the costs of analytics far beyond performance, such as licensing… Show more

Organizations looking for enterprise data warehouses (EDWs) cannot afford to base their evaluation on query price-performance alone. There’s much more to it. You also need capabilities that reduce the time needed for configuration, management, tuning, and other tasks that can take away from valuable time spent on business analytics.

This new whitepaper from McKnight Consulting Group explores factors that can reduce the costs of analytics far beyond performance, such as licensing structure, data storage, support for non-structured data, concurrency scaling, and much more. Download your copy today, and make a well-informed decision when choosing an EDW platform. Show less

We conducted an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements.

Read the full benchmark report, which provides insight to help IT professionals, DevOps engineers, platform architects and information security practitioners evaluate a Kubernetes platform optimal for running I/O… Show more

We conducted an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements.

Read the full benchmark report, which provides insight to help IT professionals, DevOps engineers, platform architects and information security practitioners evaluate a Kubernetes platform optimal for running I/O intensive Microsoft SQL Server applications. Show less

Companies rely on analytical databases for insights essential to the company survival and competitive advantage. The cloud can provide improved economics and operational simplicity, but choosing the most performant and cost-effective cloud data analytics solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard benchmark principles to evaluate the performance of three cloud-optimized analytical platforms architected for the separation of compute… Show more

Companies rely on analytical databases for insights essential to the company survival and competitive advantage. The cloud can provide improved economics and operational simplicity, but choosing the most performant and cost-effective cloud data analytics solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard benchmark principles to evaluate the performance of three cloud-optimized analytical platforms architected for the separation of compute and storage – Vertica in Eon Mode, Amazon Redshift, and an unnamed cloud data platform.

Read the report to learn how Vertica in Eon Mode:

Achieves best performance in all benchmark tests for scale and concurrency
Runs the most queries per hour, at every level of scale and concurrency
Cuts performance costs 45% – 73% over Amazon Redshift
Slashes performance costs 84% – 92% over the unnamed data cloud platform Show less

Get free access to this 30-page GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings: Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances; Microsoft SQL Server Microsoft on Azure Virtual Machines (VM). Both are installations of Microsoft SQL Server and we tested on both Windows Server OS and Red Hat Enterprise Linux OS.

This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a field test derived from the industry standard TPC Benchmark™ DS (TPC-DS) comparing five relational analytical databases based on scale-out cloud data warehouses.

Applications and their performance requirements have evolved dramatically in today’s landscape. The cloud enables enterprises to differentiate and innovate with APIs and microservices at a rapid pace. Cloud providers, like Azure, allow microservice endpoints to be cloned and scaled in a matter of minutes. The cloud offers elastic scalability compared to on-premises deployments, enabling faster server deployment and application development and less costly compute. In this paper, we reveal the… Show more

Applications and their performance requirements have evolved dramatically in today’s landscape. The cloud enables enterprises to differentiate and innovate with APIs and microservices at a rapid pace. Cloud providers, like Azure, allow microservice endpoints to be cloned and scaled in a matter of minutes. The cloud offers elastic scalability compared to on-premises deployments, enabling faster server deployment and application development and less costly compute. In this paper, we reveal the results of application performance testing we completed both with and without Azure Cache for Redis on top of Azure SQL Database and Azure Database for PostgreSQL. Show less

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations comprise a vast array of applications and systems, many of which have turned to APIs to exchange data as the glue to hold these heterogeneous artifacts together. In this paper, we reveal the results of performance testing we completed across three API and Microservices… Show more

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations comprise a vast array of applications and systems, many of which have turned to APIs to exchange data as the glue to hold these heterogeneous artifacts together. In this paper, we reveal the results of performance testing we completed across three API and Microservices Management platforms: Kong Enterprise, Apigee Edge, and Apigee Edge Microgateway. Show less

In this paper, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which tools and platforms have made, or are in the process of making, the leap to this new generation of data management and integration capabilities.

The vendors/products chosen were:

AWS (Glue)

Azure (Azure Data Factory)

Cloudera (Cloudera Replication Manager)

Fivetran

Google… Show more

In this paper, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which tools and platforms have made, or are in the process of making, the leap to this new generation of data management and integration capabilities.

The vendors/products chosen were:

AWS (Glue)

Azure (Azure Data Factory)

Cloudera (Cloudera Replication Manager)

Fivetran

Google (Alooma)

IBM

Informatica

Matillion

Oracle

SAP

Talend Show less

Data, web, and application security has evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how we build and deploy them—has changed. We’ve traded monolithic applications for microservices running in containers and communicating via application programming interfaces (APIs)—and all of it deployed through automated continuous integration/continuous deployment (CI/CD) pipelines. The frameworks we have established to build and deploy… Show more

Data, web, and application security has evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how we build and deploy them—has changed. We’ve traded monolithic applications for microservices running in containers and communicating via application programming interfaces (APIs)—and all of it deployed through automated continuous integration/continuous deployment (CI/CD) pipelines. The frameworks we have established to build and deploy applications are optimized for time to market—yet security remains of utmost importance.

Our focus is specifically on approaches to securing apps, APIs, and microservices that are tuned for high performance and availability. We define “high performance” as companies that experience workloads of more than 1,000 transactions per second (tps) and require a maximum latency below 30 milliseconds across the landscape. Show less

This authoritative report from GigaOm Research, a respected independent industry analyst firm, details the key performance and cost criteria to guide your cloud data warehouse selection.

“Price and performance are critical points of interest…our analysis reveals Avalanche to be the industry leader on this criterion.”

Highlights from the report:

Key factors to consider when evaluating a hybrid cloud data warehouse
Head-on vendor comparisons across performance and cost… Show more

This authoritative report from GigaOm Research, a respected independent industry analyst firm, details the key performance and cost criteria to guide your cloud data warehouse selection.

“Price and performance are critical points of interest…our analysis reveals Avalanche to be the industry leader on this criterion.”

Highlights from the report:

Key factors to consider when evaluating a hybrid cloud data warehouse
Head-on vendor comparisons across performance and cost using industry standard TPC-H benchmark
Vendors analyzed include Actian Avalanche, Snowflake, Amazon Redshift, Microsoft Azure Synapse and Google BigQuery
Assessment of single user and multiple concurrent users scenarios Show less

This third-party report from McKnight Consulting Group uses industry-standard benchmark principles to evaluate the performance of three cloud-optimized analytical platforms architected for the separation of compute and storage – Vertica in Eon Mode, Amazon Redshift, and an unnamed cloud data platform.

In this report, we compare the three major data pipeline platforms: Matillion, Stitch, and Fivetran; and run them through a series of selected tests that highlight their degree of automation, ease of setup, and documentation. We evaluated aspects that include the time and effort required to set up a source-destination connection, the degree of automation throughout the process, and the quality of documentation to support the effort. These areas address the three major “humps of work” we have… Show more

In this report, we compare the three major data pipeline platforms: Matillion, Stitch, and Fivetran; and run them through a series of selected tests that highlight their degree of automation, ease of setup, and documentation. We evaluated aspects that include the time and effort required to set up a source-destination connection, the degree of automation throughout the process, and the quality of documentation to support the effort. These areas address the three major “humps of work” we have encountered in our field work with data pipelines. Show less

The pace of relational analytical databases deploying in the cloud are at an all-time high. The goal of this paper is to provide information to help a customer make the best decision, looking at factors in cloud data platform pricing – such as scope, scale, deployment, etc. – and how to determine the ultimate success metric when it comes to making a decision on a cloud data warehouse deployment – price-performance.

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technology applications. Large companies and complex organizations have turned to APIs for exchanging data to knit these heterogeneous systems together and turn data into a service. In this paper, we reveal the results of performance testing we completed with four full-lifecycle API management platforms.

This report is the third in a series of enterprise roadmaps addressing cloud analytic databases. The last two reports focused on comparing vendors on key decision criteria that were targeted primarily at cloud integration. This report is an update to the 2019 Enterprise Roadmap: Cloud Analytic Databases. However, this time around we have new vendors and a new name. We’ve reviewed and adjusted our inclusion criteria. We’re now targeting the technologies that tackle the objectives of an analytics… Show more

This report is the third in a series of enterprise roadmaps addressing cloud analytic databases. The last two reports focused on comparing vendors on key decision criteria that were targeted primarily at cloud integration. This report is an update to the 2019 Enterprise Roadmap: Cloud Analytic Databases. However, this time around we have new vendors and a new name. We’ve reviewed and adjusted our inclusion criteria. We’re now targeting the technologies that tackle the objectives of an analytics program, as opposed to the means by which they are achieving these objectives. Show less

In this Business Technology Impact report, we take a look at a large multi-national firm and its implementation of a data lake and data catalog. Within the company, a small team worked to transform the data lake from an underutilized, misunderstood ‘white elephant’ into a resource that drove the company’s growth and innovation.

This report details the results of a Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), compared:

Microsoft SQL Server 2019 on an Amazon Web Services (AWS) r5a.8xlarge Elastic Cloud Compute (EC2) instance with General Purpose (gp2) volumes.
Microsoft SQL Server 2019 on an Azure E32as_v4 Virtual Machine (VM) with P30 Premium Storage drives

This report is targeted at Business and IT decision-makers as they look to implement MLOps, which is an approach to deliver Machine Learning- (ML-) based innovation projects. As well as describing how to address the impact of ML across the development cycle, it presents an approach based on maturity levels such that the organization can build on existing progress.

This report outlines the results from a Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings:

Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM).

Today, to fully harness data to gain a competitive advantage, embedded databases need a high level of performance to provide real-time processing at scale.

See SQLite, the traditional alternative to the file system approach for embedding data management into edge applications and Actian Zen perform.

See for yourself in this benchmark report by McKnight Consulting Group.

This report outlines the results from a GigaOm Analytic Field Test derived from the industry standard TPC Benchmark™ DS (TPC-DS)1 comparing Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse — four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences between the four platforms.

This benchmark did a head-to-head comparison of Actian Zen and InfluxDB for IoT time series data, both installed on a Raspberry Pi running Linux ARM/Raspbian using their native APIs (NoSQL).

If your data warehouse is under-delivering to the enterprise or if somehow you have not deployed one, you have the opportunity to deploy or shore up this valuable company resource. As a matter of fact, of all the constructs in information management, the data warehouse would be the first entity to bring to standard for maximum ROI. There are innumerable subtleties and varieties in architecture and methods. Many are appropriate in context of the situation and the requirements. We will explore… Show more

If your data warehouse is under-delivering to the enterprise or if somehow you have not deployed one, you have the opportunity to deploy or shore up this valuable company resource. As a matter of fact, of all the constructs in information management, the data warehouse would be the first entity to bring to standard for maximum ROI. There are innumerable subtleties and varieties in architecture and methods. Many are appropriate in context of the situation and the requirements. We will explore these in this report. Show less

This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available… Show more

This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available for developers as a free on-premise community edition, as a download with support in both the Amazon Web Services (AWS) and Azure marketplaces with single-click deployment. Show less

Pekin Insurance is one of the nation’s most successful insurance providers, with combined assets of $2 billion, more than 800 employees, 1,500 agencies, and 8,500 independent agents. Pekin Insurance is on the fast path to a full overhaul and modernization of their data, from the platform, to quality, to governance, to enabling consumers. They have built a 3-year strategy focusing on Data & Analytics and are wrapping up the final year, focused on a robust data layer with a data lake and a data… Show more

Pekin Insurance is one of the nation’s most successful insurance providers, with combined assets of $2 billion, more than 800 employees, 1,500 agencies, and 8,500 independent agents. Pekin Insurance is on the fast path to a full overhaul and modernization of their data, from the platform, to quality, to governance, to enabling consumers. They have built a 3-year strategy focusing on Data & Analytics and are wrapping up the final year, focused on a robust data layer with a data lake and a data warehouse, on target, on budget, and within scope. Show less

This checklist focuses on how IoT data and analytics can be used to help drive the customer experience.

Today, to fully harness data to gain a competitive advantage, embedded databases need a high level of performance to provide real-time processing at scale.

See SQLite, the traditional alternative to the file system approach for embedding data management into edge applications and Actian Zen perform.

See for yourself in this benchmark report by McKnight Consulting Group.

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations are a vast array of applications and systems, many of which have turned to APIs as the glue to hold these heterogeneous artifacts together.

This report examines the results of a performance benchmark completed with two popular API management solutions: Kong and… Show more

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations are a vast array of applications and systems, many of which have turned to APIs as the glue to hold these heterogeneous artifacts together.

This report examines the results of a performance benchmark completed with two popular API management solutions: Kong and Apigee—two full life-cycle API management platforms built with scale-out potential and architectures for large scale, high performance deployments. Despite these similarities, there are some distinct differences in the two platforms. Show less

Read this cloud analysis paper for a benchmark of Vertica and Amazon's Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

This report outlines the results from the GigaOm Analytic Field Test based on an industry standard TPC Benchmark™ H (TPC-H)1 to compare Amazon Redshift, Azure SQL Data Warehouse, Google Big Query, and Snowflake Data Warehouse—four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences in the four platforms.

This category of data is known by several names: streaming, messaging, live feeds, real-time, event-driven, and so on. This type of data needs special attention, because delayed processing can and will negatively affect its value—a sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—all can be of immense value to a decision maker, but only if he or she is alerted in time to affect the outcome.

We will introduce… Show more

This category of data is known by several names: streaming, messaging, live feeds, real-time, event-driven, and so on. This type of data needs special attention, because delayed processing can and will negatively affect its value—a sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—all can be of immense value to a decision maker, but only if he or she is alerted in time to affect the outcome.

We will introduce and demonstrate a method for an organization to assess and benchmark—for their own current and future uses and workloads—the technologies currently available. We will begin by reviewing the landscape of streaming data and message queueing technology. They are alike in purpose—process massive amounts of streaming data generated from social media, logging systems, clickstreams, Internet-of-Things devices, and so forth. However, they also have a few distinctions, strengths, and weaknesses. Show less

Today, to fully harness data to gain a competitive advantage, embedded databases need a high level of performance to provide real-time processing at scale.

SQLite, the traditional, but now obsolete, alternative to the file system approach for embedding data management into edge applications, just can’t keep up with Actian Zen.

See for yourself in this benchmark report by McKnight Consulting Group.

Read this cloud analysis paper for a benchmark of Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

The world of data is rapidly changing. Data is the prime foundational component of any meaningful corporate initiative. Managing and evaluating this prime asset is ongoing continually in competitive organizations. The incorporation of new information into this process is required, and tradeoffs must be considered in the decision-making process.

Last year this report focused on comparing vendors on key decision criteria that were primarily targeted at cloud integration. The vectors… Show more

The world of data is rapidly changing. Data is the prime foundational component of any meaningful corporate initiative. Managing and evaluating this prime asset is ongoing continually in competitive organizations. The incorporation of new information into this process is required, and tradeoffs must be considered in the decision-making process.

Last year this report focused on comparing vendors on key decision criteria that were primarily targeted at cloud integration. The vectors represented how well the products provided the features of the cloud that corporate customers have come to expect. In 2017 we chose products with cloud analytic databases that exclusively deploy in the cloud, or had undergone major renovation for cloud deployments. This report is an update to the 2017 Sector Roadmap: Cloud Analytic Databases and, as such, continues with an analysis of the same vendors. Show less

In today’s data driven world, where effective decisions are based on a company’s ability to access information in seconds or minutes rather than hours or days, selecting the right analytical database platform is critical.

Read this McKnight white paper to learn:

Which criteria to consider for an analytical database
The process for transitioning away from PostgreSQL
Transition success stories from Etsy, TravelBird and Nimble Storage

We conducted this benchmark study, which focuses on the performance of cloud-enabled1 , enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Amazon Redshift. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

We conducted this benchmark study, which focuses on the performance of cloud-enabled , enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Microsoft SQL Server. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance at scale.

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Impala. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Snowflake. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

The benefits of modern data architecture are as follows:

It ensures the ability of the data analysis function of the organization to actually do analysis rather than restrict it to data hunting and preparation almost exclusively.
It provides the ability to maneuver as an organization in the modern era of information competition with consistent, connected data sets with every data set playing a mindful and appropriate role.
It enables a company to measure and improve the business… Show more

The benefits of modern data architecture are as follows:

It ensures the ability of the data analysis function of the organization to actually do analysis rather than restrict it to data hunting and preparation almost exclusively.
It provides the ability to maneuver as an organization in the modern era of information competition with consistent, connected data sets with every data set playing a mindful and appropriate role.
It enables a company to measure and improve the business with timely key performance indicators, such as streamlining your supply chain or opening up new markets with new products and services supported by technology built for analytics.
This paper will help an organization understand the value of modernizing its data architecture and how to frame a modernization effort that delivers analysis capabilities, diverse yet connected data, and key performance measures. Show less

Organizations rely on Big Data platforms to analyze large volumes of data from a variety of sources to derive timely insights on everything from fraud detection, to customer churn, predictive maintenance and more. As more organizations move this data to the cloud for improved economics and operational simplicity, choosing the most performant and cost-effective data analytical solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard data… Show more

Organizations rely on Big Data platforms to analyze large volumes of data from a variety of sources to derive timely insights on everything from fraud detection, to customer churn, predictive maintenance and more. As more organizations move this data to the cloud for improved economics and operational simplicity, choosing the most performant and cost-effective data analytical solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard data benchmark principles to evaluate the performance of two cloud-optimized data analytical solutions architected for the separation of compute and storage — Vertica in Eon Mode and Snowflake Computing. Show less

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. This benchmark study focuses on the performance of cloud-enabled, enterprise-ready, relationally based, analytical workload solutions from Microsoft Azure SQL Data Warehouse and Amazon Redshift.

The benchmark tested the scalability of corporate-complex workloads in terms of data volume with 30TB of data. The testing was conducted using as similar a… Show more

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. This benchmark study focuses on the performance of cloud-enabled, enterprise-ready, relationally based, analytical workload solutions from Microsoft Azure SQL Data Warehouse and Amazon Redshift.

The benchmark tested the scalability of corporate-complex workloads in terms of data volume with 30TB of data. The testing was conducted using as similar a configuration as can be achieved across Azure and Amazon Web Services (AWS) offerings. Show less

This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise.

Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.

In this paper, I will review information’s importance to business, connect data architecture to business success, define data maturity and discuss how to architect information and improve data maturity efficiently with an Intelligent Data Platform.

The Informatica Intelligent Data Platform (IDP) is an integrated end-to-end data management platform to spur data maturity and enable business initiatives with the right data at the right time. IDP also aims to decrease complexity by providing… Show more

In this paper, I will review information’s importance to business, connect data architecture to business success, define data maturity and discuss how to architect information and improve data maturity efficiently with an Intelligent Data Platform.

The Informatica Intelligent Data Platform (IDP) is an integrated end-to-end data management platform to spur data maturity and enable business initiatives with the right data at the right time. IDP also aims to decrease complexity by providing a unified platform for enterprise data, connectivity, metadata, and operations. This brings the entire realm of data management under a single umbrella. Show less

This Sector Roadmap is focused on unstructured data management tool selection for multiple uses across the enterprise. We eliminated any products that may have been well-positioned and viable for limited or non-analytical uses, such as log file management, but deficient in other areas. Our selected use cases are designed for high relevance for years to come and so the products we chose needed to match all these uses. In general, we recommend that an enterprise only pursue an unstructured data… Show more

This Sector Roadmap is focused on unstructured data management tool selection for multiple uses across the enterprise. We eliminated any products that may have been well-positioned and viable for limited or non-analytical uses, such as log file management, but deficient in other areas. Our selected use cases are designed for high relevance for years to come and so the products we chose needed to match all these uses. In general, we recommend that an enterprise only pursue an unstructured data management tool capable of addressing a majority or all of that enterprises’ use cases.

In this Sector Roadmap, vendor solutions are evaluated over five Disruption Vectors: query operations, search capabilities, deployment options, data management features, and schema requirements. Show less

This Sector Roadmap is focused on master data management (MDM) selection for multiple data domains across the enterprise. In this Sector Roadmap, vendor solutions are evaluated over seven Disruption Vectors: cloud offerings, collaborative data management, going beyond traditional hierarchies, big data integration, machine learning-enabled, APIs and data-as-a-service, and onboard analytics.

GigaOM membership/fee required.

This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise.

Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.

GigaOM membership/fee required.

This is NOT a white paper (except that there is documentation) but rather it’s a test drive for predictive maintenance – something on the minds of many these days – we built.

Experience how HPE Vertica enables you to store in near real time sensor data from multiple cooling towers across the USA and predict equipment failure ahead of time to provide continuity of service. In this AWS Test Drive, we will create an instance of the Vertica Cluster and generate readings from multiple… Show more

This is NOT a white paper (except that there is documentation) but rather it’s a test drive for predictive maintenance – something on the minds of many these days – we built.

Experience how HPE Vertica enables you to store in near real time sensor data from multiple cooling towers across the USA and predict equipment failure ahead of time to provide continuity of service. In this AWS Test Drive, we will create an instance of the Vertica Cluster and generate readings from multiple cooling towers in real time that are stored in Vertica. The test drive also includes a web based dashboard that interacts with Vertica to leverage machine learning algorithm such as logistic regression to predict risk of failure to prevent down-time. You will have 4 hours to play, query and analyze the dataset.

Choose the “HPE Vertica Predictive Maintenance Testing Trial” at the link. Show less

This is a series of 4 blog posts.

If you’re a software vendor moving to a SaaS business model either by creating new product lines (from scratch or by adding cloud characteristics to existing products) or converting an existing product portfolio, the transition to a SaaS model will impact every aspect of the company right down to the company’s DNA.

In these posts, William addresses the top four considerations for choosing the database in the move. The database selection is… Show more

This is a series of 4 blog posts.

If you’re a software vendor moving to a SaaS business model either by creating new product lines (from scratch or by adding cloud characteristics to existing products) or converting an existing product portfolio, the transition to a SaaS model will impact every aspect of the company right down to the company’s DNA.

In these posts, William addresses the top four considerations for choosing the database in the move. The database selection is critical and acts as a catalyst for all other technology decisions. The database needs to support both the immediate requirements as well as future, unspecified and unknown requirements. Ideally the DBMS selection should be one of the first technology decisions made for the move. Show less

Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical… Show more

Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs.

According to market research, through 2020, spending on cloud-based Big Data Analytics technology will grow 4.5x faster than spending for on-premises solutions. Due to the economics and functionality, use of the cloud should now be a given in most database selections. The factors driving data projects to the cloud are many.

Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?” This paper will reflect on the top decision points in determining what depth to move into the cloud and what you need to do in order to be successful in the move. This could be a move of an existing analytical workload or the move of the organization to the cloud for the first time. It’s “everything but the product selection.”

GigaOM membership/fee required. Show less

Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs… Show more

Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs. Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?”

This RFI will reflect on the top questions you should ask in making your product selection when moving information management and your analytical workload to the cloud.

GigaOM membership/fee required. Show less

Companies everywhere are realizing that data is a key asset that can directly impact business goals. Yet, in some enterprises, awareness of data’s value doesn’t translate into increased data maturity and modernization. Often treated as a drag-along to budgeted applications, data architecture can be accidental or happenstance—a casualty of a lack of focus. The opportunity now exists to influence the future and undertake highly data-focused projects in more modern, scalable, and usable ways. In… Show more

Companies everywhere are realizing that data is a key asset that can directly impact business goals. Yet, in some enterprises, awareness of data’s value doesn’t translate into increased data maturity and modernization. Often treated as a drag-along to budgeted applications, data architecture can be accidental or happenstance—a casualty of a lack of focus. The opportunity now exists to influence the future and undertake highly data-focused projects in more modern, scalable, and usable ways. In this Ten Mistakes to Avoid, William McKnight identifies the misguided practices that cause the most friction in modernization efforts and the journey to higher data maturity. He offers tips on how to mature the environment that supports the asset upon which competition is forged today—data.

TDWI membership required. Show less

This study, written by industry analyst Richard Hackathorn of Bolder Technology, Inc. and William McKnight of McKnight Consulting, examines the business value that Teradata Business Analytics Consulting engagements generate for client companies. Based on case studies from different industries, key insights and trends behind this value generation are documented, as well as recommendations for pursuing successful business analytics consulting engagements.

Read this cloud analysis paper for a benchmark of HPE Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

Big data analytics platforms load, store, and analyze volumes of data at high speed, providing insight. This data is structured, semi-structured, or unstructured from a variety of sources. Data-driven organizations are leveraging this data analysis to market new promotions, for… Show more

Read this cloud analysis paper for a benchmark of HPE Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

Big data analytics platforms load, store, and analyze volumes of data at high speed, providing insight. This data is structured, semi-structured, or unstructured from a variety of sources. Data-driven organizations are leveraging this data analysis to market new promotions, for operational efficiency, and to evaluate risk and detect fraud.

Show less

Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs.

This paper, written by McKnight Consulting analysts William McKnight… Show more

Recent trends in information management see companies shifting their focus to, or entertaining a notion for the first time of a cloud-based solution. In the past, the only clear choice for most organizations has been on-premises data—oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all or some of a company’s analytical needs.

This paper, written by McKnight Consulting analysts William McKnight and Jake Dolezal, describes two organizations with mature enterprise data warehouse capabilities, that have pivoted components of their architecture to accommodate the cloud.

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9 Cloud analytic database solutions were evaluated over six Disruption Vectors: Robustness of SQL, Built-in optimization, On-the-fly elasticity, Dynamic Environment Adaption, Separation of compute from storage, and Support for diverse data.

Key findings in our analysis include:

Due to the economics and functionality, use of the cloud can now be a given in most database selection in 2017 and beyond.
Several offerings have been able to leapfrog databases with much more history by… Show more

9 Cloud analytic database solutions were evaluated over six Disruption Vectors: Robustness of SQL, Built-in optimization, On-the-fly elasticity, Dynamic Environment Adaption, Separation of compute from storage, and Support for diverse data.

Key findings in our analysis include:

Due to the economics and functionality, use of the cloud can now be a given in most database selection in 2017 and beyond.
Several offerings have been able to leapfrog databases with much more history by being “born in the cloud” and tightly integrating with it through On-the-fly elasticity, Dynamic Environment Adaption, and Separation of compute from storage.
While traditional database functionality is still required, cloud dynamics are causing the need for more Robustness of SQL, Support for diverse data and other capabilities that may not be present in traditional databases. Show less

The study evaluated RedPoint Data Management™ as part of its ongoing research into different approaches and architectures for Hadoop data integration.

DOWNLOAD THE REPORT AND FIND OUT:

Which technology approach completed the workload 1,900 percent faster.
Which technology approach excelled at managing high-volume data in demanding customer data applications.
What the primary performance advantages are of an architecture built on YARN.
Download this informative report… Show more

The study evaluated RedPoint Data Management™ as part of its ongoing research into different approaches and architectures for Hadoop data integration.

DOWNLOAD THE REPORT AND FIND OUT:

Which technology approach completed the workload 1,900 percent faster.
Which technology approach excelled at managing high-volume data in demanding customer data applications.
What the primary performance advantages are of an architecture built on YARN.
Download this informative report today! Show less

This report serves end-user companies seeking to step into the world of Hadoop, move an existing Hadoop strategy into profitability or production status.

Cost savings combined with the ability to execute the complete application at scale are strong motivators for adopting Hadoop. This report cuts out all the non-value-added noise about Hadoop and presents a minimum viable product (MVP) for building a Hadoop cluster for the enterprise that is both cost-effective and scalable.

Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution:

-Complies with (and even extends) enterprise standards
-Integrates seamlessly with the existing… Show more

Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution:

-Complies with (and even extends) enterprise standards
-Integrates seamlessly with the existing information infrastructure
-Fills a critical role within enterprise architecture.

Integrating Hadoop covers the gamut of the setup, architecture and possibilities for Hadoop in the organization, including:
-Supporting an enterprise information strategy
-Organizing for a successful Hadoop rollout
-Loading and extracting of data in Hadoop
-Managing Hadoop data once it's in the cluster
-Utilizing Spark, streaming data, and master data in Hadoop processes - examples are provided to reinforce concepts. Show less

I’ve spent my career looking at how large quantities of complex information affects every part of our lives and this is the most exciting time to be doing that. Information affects finances. Information affects your health. It affects the life choices presented to you. It cannot be overstated how important the accumulation of enormous sums of detailed data about all of us and every aspect of business is.

This ebook looks at the rise of machine data, the future sources of digital data… Show more

I’ve spent my career looking at how large quantities of complex information affects every part of our lives and this is the most exciting time to be doing that. Information affects finances. Information affects your health. It affects the life choices presented to you. It cannot be overstated how important the accumulation of enormous sums of detailed data about all of us and every aspect of business is.

This ebook looks at the rise of machine data, the future sources of digital data and the technologies for enterprises to deploy now to be ready for the data economy. Show less

With its support for batch and real-time data processing, Spark is one of the most exciting new tools in the big data space. And data integration is still the key “ingredient” that brings a variety of sources together, including real-time big data.

A new report by MCG Global Services evaluates leading integration vendors, and benchmarks their performance against two key criteria: The depth of Hadoop integration and The performance of integration jobs.

The report notes that these… Show more

With its support for batch and real-time data processing, Spark is one of the most exciting new tools in the big data space. And data integration is still the key “ingredient” that brings a variety of sources together, including real-time big data.

A new report by MCG Global Services evaluates leading integration vendors, and benchmarks their performance against two key criteria: The depth of Hadoop integration and The performance of integration jobs.

The report notes that these key differentiators "could spell the difference for a 'just-in-time' answer to a business question and a 'too-little-too-late' result."
Show less

Enterprise data warehouse (EDW) and business intelligence (BI) practices have been developing rapidly over the past couple of decades. Vendors have developed different ways to give organizations an alternative to “starting from scratch.” Packaged BI and reporting templates are examples of pre-built, pre-designed tools that a company can adopt to move their analytic train down the line quicker and easier. IBM synthesized its knowledge and expertise of the information needs specific to several… Show more

Enterprise data warehouse (EDW) and business intelligence (BI) practices have been developing rapidly over the past couple of decades. Vendors have developed different ways to give organizations an alternative to “starting from scratch.” Packaged BI and reporting templates are examples of pre-built, pre-designed tools that a company can adopt to move their analytic train down the line quicker and easier. IBM synthesized its knowledge and expertise of the information needs specific to several industries. The result was the development of a set of Industry Data Models that leverage their expertise and best practices. Show less

The communications service provider (CSP) industry has undergone a dramatic shift in recent years. The traditional model of competing on subscription plans is no longer an adequate business strategy. Since most internal systems were built with this model in mind, these environments, with non-enriched, non-integrated, and latent data, fit for after-the-fact reporting, are struggling to keep up with the changes.

This research report will explain how CSPs establish a framework for their… Show more

The communications service provider (CSP) industry has undergone a dramatic shift in recent years. The traditional model of competing on subscription plans is no longer an adequate business strategy. Since most internal systems were built with this model in mind, these environments, with non-enriched, non-integrated, and latent data, fit for after-the-fact reporting, are struggling to keep up with the changes.

This research report will explain how CSPs establish a framework for their analytics as well as review the business drivers for telcos and the key benefits that big data analytics provide. It will also address the impact of the business drivers and the advantages of streaming analytics, combined with the ability to harness big data to meet several CSP competitive requirements. It will conclude by summarizing this comprehensive big data analytics framework for CSPs. Show less

The NoSQL Evaluator's Guide presents a framework for evaluating NoSQL databases for the enterprise. It begins with an overview of NoSQL technology, features and models, and concludes with evaluation criteria.

In this NoSQL Evaluator's Guide, you’ll learn:

What NoSQL databases have in common
The main types of NoSQL databases
Top three evaluation criteria: scalability, performance and agility
Learn how to compare NoSQL databases like Couchbase, MongoDB and DataStax

Data is crucial in today's business world and making smart data decisions upfront provides great leverage for successful long-term business results.

This ebook, written by Jim Harris of OCDQ Blog, pulls the insights from his interview with William McKnight, an internationally recognized authority in information management.

Fully utilized big data can give organizations a competitive advantage if they can get it to decision-makers, or what we call the “executive edge.” But getting big data to that demographic requires the business intelligence (BI) community to help executives overcome their big-data dissonance.

This report provides the BI community with information it can use to help executives use big data effectively and at the same time raise their expectations about what they should expect from the BI… Show more

Fully utilized big data can give organizations a competitive advantage if they can get it to decision-makers, or what we call the “executive edge.” But getting big data to that demographic requires the business intelligence (BI) community to help executives overcome their big-data dissonance.

This report provides the BI community with information it can use to help executives use big data effectively and at the same time raise their expectations about what they should expect from the BI community. Show less

This white paper discusses the challenges most companies face with managing their data and suggest some strategies and solutions to turn midmarket data into a working asset.

Being skillful in data integration allows an enterprise to maximize business performance; however, your productivity and operational costs vary depending which enterprise tool you select.

This paper by William McKnight highlights modern data integration challenges, the choices involved in tool selection, a product selection checklist, and reasons why Talend Enterprise Data Integration should be considered for your next data integration project.

Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart… Show more

Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together.

The practical, hands-on guidance in this book includes:

Part 1: The importance of information management and analytics to business, and how data warehouses are used
Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality
Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data
Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management

Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. Show less

Businesses today need to consider the diminishing nature of what is unknown. With more data under management than ever before, organizations are increasingly embracing analytics as a way to predict behavior and intervene to achieve better results.

While few would argue with the notion of self-service business analytics, it takes considerable discipline, tactics and software to make it happen. Setting up the self-service business analytics environment requires special consideration of… Show more

Businesses today need to consider the diminishing nature of what is unknown. With more data under management than ever before, organizations are increasingly embracing analytics as a way to predict behavior and intervene to achieve better results.

While few would argue with the notion of self-service business analytics, it takes considerable discipline, tactics and software to make it happen. Setting up the self-service business analytics environment requires special consideration of many factors. This paper lists the top five. Show less

Lost amid the conversation on big data and the accelerating advancement of just about every aspect of enterprise software that manages information are the things that hold it all together. Yet this is critical: information-management components must come together in a meaningful fashion or there will be unneeded redundancy and waste and opportunities missed. Considering that optimizing the information asset goes directly to the organization’s bottom line, it behooves us to play an exceptional… Show more

Lost amid the conversation on big data and the accelerating advancement of just about every aspect of enterprise software that manages information are the things that hold it all together. Yet this is critical: information-management components must come together in a meaningful fashion or there will be unneeded redundancy and waste and opportunities missed. Considering that optimizing the information asset goes directly to the organization’s bottom line, it behooves us to play an exceptional game— not a haphazard one—with our technology building blocks. Show less

This white paper provides guidance around information management data store category selection including the NoSQL movement and how to prepare for success in this new ecosystem – not the least of which is to have robust data integration capabilities for the heterogeneous environment. As one senior technology leader recently said to me: “There are a million architectures today for my data.” How true!

Analytics are forming the basis of competition today. This white paper addresses what distinguishes analytics and answers the question are we doing analytics in the data warehouse? The paper further talks about the contending Platforms for the Analytics Workload and introduces the ParAccel Analytic Database as a key component of Information Architecture.

The unique innovation by Teradata, in Teradata 14, is to add columnar structure to a table, effectively mixing row structure, column structure and multi-column structure directly in the DBMS which already powers many of the largest data warehouses in the world. With intelligent exploitation of Teradata Columnar in Teradata 14k, there is no longer the need to go outside the data warehouse DBMS for the power of performance that columnar provides, and it is no longer necessary to sacrifice… Show more

The unique innovation by Teradata, in Teradata 14, is to add columnar structure to a table, effectively mixing row structure, column structure and multi-column structure directly in the DBMS which already powers many of the largest data warehouses in the world. With intelligent exploitation of Teradata Columnar in Teradata 14k, there is no longer the need to go outside the data warehouse DBMS for the power of performance that columnar provides, and it is no longer necessary to sacrifice robustness and support in the DBMS that holds the post-operational data. Show less

Communications companies are currently embroiled in a series of initiatives to enable the convergence of systems to deliver converged services – the utilization of a single network to transport all information and services (voice, data and video) by encapsulating the data into packets. Providing converged services is rendering inadequate the isolated systems that are in place in many of these companies. It does not take long in this systems evolution to be reminded of the immense importance… Show more

Communications companies are currently embroiled in a series of initiatives to enable the convergence of systems to deliver converged services – the utilization of a single network to transport all information and services (voice, data and video) by encapsulating the data into packets. Providing converged services is rendering inadequate the isolated systems that are in place in many of these companies. It does not take long in this systems evolution to be reminded of the immense importance of access to high-quality, well-performing and integrated information in support of the transition. Learn how the analytic database supports the needs of Converged Systems. Show less

Columnar databases are becoming an essential component of an enterprise infrastructure for the storage of data designed to run specific workloads. When an organization embraces the value of performance, it must do everything it can to remove barriers to the delivery of the right information at the right time to the right people and systems. There is no "ERP" for post-operational data. No one-size-fits-all system. Some gave that role to the relational, row-based data warehouse, but that ship has… Show more

Columnar databases are becoming an essential component of an enterprise infrastructure for the storage of data designed to run specific workloads. When an organization embraces the value of performance, it must do everything it can to remove barriers to the delivery of the right information at the right time to the right people and systems. There is no "ERP" for post-operational data. No one-size-fits-all system. Some gave that role to the relational, row-based data warehouse, but that ship has sailed. In addition to columnar databases, very-large data stores like Hadoop, real-time stream processing, and data virtualization are required today to bring together result sets across all data systems. This paper focuses on conveying an understanding of columnar databases and the proper utilization of columnar databases within the enterprise. Show less

This white paper is intended to provide a consolidated starting point for information technology managers who need to select systems to store retrievable analytic data for their business. The paper covers recommended use of information stores including relational row-based data warehouses and marts, multi-dimensional databases, columnar databases and MapReduce.

In the data modeling area, rather than starting out with the grandiose goal of building the enterprise data model, as if it were a respectable end in itself, to be successful, data warehouse teams must leave the spotlight firmly on the business deliverables. The data model, being a means to an end, is grounded in reality and constructed through a series of iterative progressions, staying in synch and not ahead of the partner components.

Co-authored with Dan McCreary. NoSQL is a new and fast-growing category of data management technologies that uses non-relational database architectures (hence NoSQL, or Not-Only SQL). NoSQL is not the best solution for every data management requirement, however it is often better suited to handle the requirements of high-performance, web-scalable systems and big data analysis. Organizations like Facebook, Twitter, Netflix and Yahoo are notable examples of innovators which have used NoSQL… Show more

Co-authored with Dan McCreary. NoSQL is a new and fast-growing category of data management technologies that uses non-relational database architectures (hence NoSQL, or Not-Only SQL). NoSQL is not the best solution for every data management requirement, however it is often better suited to handle the requirements of high-performance, web-scalable systems and big data analysis. Organizations like Facebook, Twitter, Netflix and Yahoo are notable examples of innovators which have used NoSQL solutions to gain greater scale and performance, and at a fraction of the cost of traditional relational database systems. Show less

Mobile business intelligence is a process, not a project, and a journey rather than a destination. The case studies included represent two forms that mobile business intelligence can take to empower the mobile worker and port existing applications. This paper discusses two different companies, their environments, reasons for going mobile, and key success factors. The examples provide a framework of information architecture evaluation reference points, lay out options for mobile business… Show more

Mobile business intelligence is a process, not a project, and a journey rather than a destination. The case studies included represent two forms that mobile business intelligence can take to empower the mobile worker and port existing applications. This paper discusses two different companies, their environments, reasons for going mobile, and key success factors. The examples provide a framework of information architecture evaluation reference points, lay out options for mobile business intelligence, and provide best practices for those considering, planning, or doing some form of mobile business intelligence evaluation. Show less

More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise's core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple… Show more

More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise's core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data. Show less

As companies take steps to manage their information asset, choosing a platform and database management system (DBMS) is absolutely fundamental. In fact, the platform is the foundation of architecture and business intelligence and the starting point for tool selection, consultancy hires, and more. In short, a company's platform is key in defining its information culture.

Interested in entering the consulting business? Already have a consulting practice but want to take its profits to the next level? 90 Days to Success in Consulting provides an action plan for success in the ultra-competitive consulting industry. The book is designed to logically take you through the major consulting topics and provide action items to be done in the next 90 days for immediate business functions, as well as for planning the future phases of your consulting journey. The book… Show more

Interested in entering the consulting business? Already have a consulting practice but want to take its profits to the next level? 90 Days to Success in Consulting provides an action plan for success in the ultra-competitive consulting industry. The book is designed to logically take you through the major consulting topics and provide action items to be done in the next 90 days for immediate business functions, as well as for planning the future phases of your consulting journey. The book covers the various opportunities available, including the traps and pitfalls to avoid, ensuring a successful career as a consultant. Show less

Diamanti Provides Fastest SQL Server Performance at the Lowest TCO
Diamanti’s customers have voiced their need for a solution to best deploy containerized Microsoft SQL Server across hybrid cloud. As part of this effort, we’ve commissioned the McKnight Group, an independent consulting firm, to conduct an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited… Show more

Diamanti Provides Fastest SQL Server Performance at the Lowest TCO
Diamanti’s customers have voiced their need for a solution to best deploy containerized Microsoft SQL Server across hybrid cloud. As part of this effort, we’ve commissioned the McKnight Group, an independent consulting firm, to conduct an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements. Show less

Recently, several architectural patterns have emerged that decentralize most components of the enterprise analytics architecture. Data lakes are a large part of that advancement.

A field test was devised to determine the differences between two popular enterprise data architectural patterns: a modern cloud data warehouse based on a Snowflake architecture and a modern data lakehouse with a Starburst-based architecture. The test was created with multiple components to determine the… Show more

Recently, several architectural patterns have emerged that decentralize most components of the enterprise analytics architecture. Data lakes are a large part of that advancement.

A field test was devised to determine the differences between two popular enterprise data architectural patterns: a modern cloud data warehouse based on a Snowflake architecture and a modern data lakehouse with a Starburst-based architecture. The test was created with multiple components to determine the differences in performance and capability, as well as the amount of time and effort required to migrate to these systems from a legacy environment. Show less