Rodrigo Prado’s Post

Amazon DataZone now integrates with AWS Lake Formation hybrid access mode to simplify secure and governed data sharing in the AWS Glue Data Catalog. This helps customers use Amazon DataZone for data access control across on-premises and cloud data lakes. #aws #awscloud #cloud #amazondatazone #announcements #awsglue #awslakeformation

Curious about how to build a multi-cloud resource data lake for SecOps, ITOps, and FinOps functions and how to normalize the data to perform analytics against it? Check out this Amazon Web Services (AWS) bog post that details how to avoid operational challenges with applications distributed across a cloud estate that spans not only multiple Cloud Service Providers (CSPs) but also regions and accounts on AWS (or Subscriptions and Projects on other providers).

🚨 Announcing the newest addition to our data sharing platform 🚨 #Redshift Sharing on Bobsled. Teams that manage data products in the cloud can now securely share data directly to customers in Amazon Redshift using its native #zerocopysharing protocol. With Redshift on Bobsled, you can: - Securely share data to customers in any region of Redshift from Snowflake, BigQuery, AWS S3, Azure Storage, Google Cloud Storage. - Unify and streamline delivery operations across data platforms, cloud storage and SFTP. - Automate fulfillment using the Bobsled API Learn more about how Bobsled supports universal data sharing to Redshift from any major cloud data lake or warehouse here https://hubs.li/Q02Dz8cD0

As data analytics use cases grow, scalability and concurrency become crucial. Amazon Redshift Serverless provides a fully managed, auto scaling cloud data warehouse for high-performance analytics. #aws #awscloud #cloud #advanced300 #amazonredshift #technicalhowto #thoughtleadership

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

Organizations struggle to efficiently extract insights from diverse data sources across infrastructure. Amazon Athena allows querying data in S3 without infrastructure. #aws #awscloud #cloud #advanced300 #amazonq #generativeai #technicalhowto

There are many very useful services on AWS that don't require you to spin up any VMs or setup anything else to use. One thing that is key to using AWS (and other cloud providers) is understanding how billing works and make sure you setup alarms for billing and use tools like Cost Anomaly Detection to help notice unexpected charges. This article from José David Arévalo is a very interesting read about a case where costs for the Amazon Athena service spun out of control for them and some tips on how to avoid something like this happening to you. Amazon Athena is a serverless query engine that allows you to run SQL-like queries on data sitting in S3 buckets. It is a great tool that allows you to look through your data without setting up an actual database. https://lnkd.in/eWB5wHq5

Data analytics today is among the fastest-growing fields as it provides significant value to business by helping make informed and data-driven decisions. Despite being a useful tool, large-scale data analytics can also cost a lot to properly implement. Fortunately, cloud platforms like AWS have a vast catalog of tools and strategies to optimize costs while helping to utilize the power of big data to the fullest. This article explores key approaches for achieving cost-efficiency in large-scale data analytics with the help of AWS solutions.

In this post, we discussed a comprehensive solution for organizations looking to implement multi-cloud data lake analytics using Athena, enabling a consolidated view of data across diverse cloud data stores and enhancing decision-making capabilities. We focused on querying data lakes across Amazon S3, Azure Data Lake Storage Gen2, and Google Cloud Storage using Athena. We demonstrated how to set up resources on Azure, GCP, and AWS, including creating databases, tables, Lambda functions, and Athena data sources. We also provided instructions for querying federated data sources from Athena, demonstrating how you can run multi-cloud queries tailored to your specific needs. Lastly, we discussed cost analysis using AWS cost allocation tags.

This post discusses implementing Aurora ML performance optimizations for real-time inference at scale against a SageMaker endpoint. It shows using SQL triggers to orchestrate predictive workloads without additional services. #aws #awscloud #cloud #amazonaurora #amazonsagemaker #amazonsagemakerautopilot #intermediate200 #technicalhowto