Stanley Chukwuemeke’s Post

AWS DMS provides data validation capabilities when migrating data to AWS. This post explores the benefits, configurations, and use cases of the AWS DMS data validation feature. #aws #awscloud #cloud #advanced300 #awsdatabasemigrationservice #technicalhowto

The #CDMC Key Controls Framework was published primarily for cloud service providers and technology providers. The framework describes 14 key controls that providers can implement to let their customers effectively manage and govern sensitive data in the cloud. https://lnkd.in/dqAywAkY

Data governance is a key enabler for adopting a data-driven culture to drive innovation. Amazon DataZone is a managed data service that makes it easier to catalog, discover, share, and govern data across AWS, on premises, and third-party sources. #aws #awscloud #cloud #advanced300 #amazondatazone #technicalhowto

As enterprises accelerate their cloud migration journeys, ensuring data parity has emerged as a critical success factor. But why is it so crucial? 1) Data parity enables precise comparisons between on-premises and cloud environments 2) It builds user confidence in the migration process 3) It helps identify and resolve discrepancies early I am thrilled to share below blog post describing how customers can use AWS Glue Data Quality to perform data parity checks during migration. Special thanks for my colleagues Arunabha Datta and Charishma Ravoori for collaboration on this. Thank you Noritaka Sekiyama and Shiv Narayanan for your feedback and support. #AWS #DataQuality #AWSGlueDataQuality https://lnkd.in/e3d-wwQB

PwC is proud to be an Amazon Web Services (AWS) Premier Tier Services Partner with AWS Competencies in Data and Analytics, DevOps, Security, and many more key areas of cloud computing. Their enterprise data platform built on Amazon Web Services (AWS) makes it easier to centralize your business data and make it more accessible for stakeholders. Don't let setting up a cloud footprint intimidate you. Let PwC help simplify the process. #PwC #AWS #cloudcomputing #dataplatform #DevOps #security #dataanalytics

What is #Cloud #DataArchitecture? A definition by Philip Russom, Ph.D.: Published on Solutions Review #ThoughtLeader; the resource hub of #ThoughtLeadership content on InsightJam.com. #CloudData #EnterpriseArchitecture

This post covers setting up trusted identity propagation between AWS IAM Identity Center, Amazon Redshift, and AWS Lake Formation on separate accounts. It also covers cross-account S3 data lake sharing using Lake Formation to enable Redshift analytics, and using QuickSight for insights. #aws #awscloud #cloud #amazonquicksight #amazonredshift #awsglue #awsiamidentitycenter #awslakeformation

In this post, we demonstrate how you can speed up database migrations by using AWS DMS parallel load options to make data loading more efficient for selected relational tables, views, and collections. #AWS #AmazonWebServices #AWSBlogs #Cloud #CloudComputing https://lnkd.in/dyKRJBRY

Your data, where you want it (Public Cloud, Hybrid Cloud, On-prem), backed with Open standards of Apache Iceberg and Apache Polaris (incubating). What is not to love? Take a look at what Dremio is doing to lead the Iceberg Lakehouse Market! https://lnkd.in/gZxBDSQC

##OCTOBERLEARNS##HIVEINCLOUD## 🚀 Hive in the Cloud: The Future of Big Data Analytics ☁️ Curious about why Hive on the cloud is a game-changer for big data? Here are unbeatable advantages of using Hive in the Cloud that will transform how you manage and analyze your data! 👇 1️⃣ Scalability at Its Best:- ⭐ Hive in the cloud provides limitless scalability, allowing you to scale your storage and compute power as your data grows—no hardware constraints! 2️⃣ Cost Efficiency:- ⭐ Pay only for what you use! With cloud infrastructure, Hive’s compute and storage resources are optimized for cost savings, perfect for managing large datasets without breaking the bank. 3️⃣ Faster Query Performance:- ⭐ Elastic cloud resources boost Hive’s query performance by dynamically allocating resources, ensuring quicker results even for the most complex queries. 4️⃣ Simplified Infrastructure Management:- ⭐ Forget about managing physical servers! Hive in the cloud offers fully managed services, so you can focus on analytics while the cloud provider handles the infrastructure. 5️⃣ Data Security & Compliance:- ⭐ Leading cloud platforms offer top-notch security features like encryption, multi-factor authentication, and compliance with global regulations (GDPR, HIPAA). 6️⃣ High Availability & Fault Tolerance:- ⭐ Cloud platforms provide automatic replication and failover capabilities, ensuring Hive workloads are highly available and fault-tolerant. 7️⃣ Global Access & Collaboration:- ⭐ With Hive in the cloud, access your data anytime, anywhere, and enable real-time collaboration across teams, no matter where they are in the world. 8️⃣ Elastic Storage:- ⭐ Store unlimited data in a variety of formats—structured, semi-structured, or unstructured—with cloud-based storage solutions like S3, Google Cloud Storage, or Azure Blob. 9️⃣ Seamless Integration with Cloud Ecosystems:- ⭐ Hive in the cloud easily integrates with other cloud-native services like machine learning, AI, and real-time analytics tools to supercharge your data projects. 🔟 Faster Deployment:- ⭐ Deploy Hive in the cloud within minutes, eliminating the time-consuming process of setting up on-premise hardware. 1️⃣1️⃣ Flexibility with Data Formats:- ⭐ Hive in the cloud supports multiple file formats like ORC, Parquet, AVRO, and more, allowing you to choose the best format for your data. 1️⃣2️⃣ Auto-scaling Capabilities:- ⭐ Automatically scale resources up or down depending on your workload, ensuring optimal performance without manual intervention. . 💡 Ready to take your big data analytics to the next level? ☁️ 🔗 Follow me for more insights on cloud computing, data engineering, and the future of data analytics! 🚀 #CloudComputing #HiveInTheCloud #BigData #DataEngineering #CloudData #ETL #TechTrends #DataAnalytics #OctoberLearns #Hadoop #Sustainability #Seekho Bigdata Institute #Karthik K.📘📘