Category: Expert (400)

In this post, we explore the cost improvements we observed when benchmarking Apache Spark jobs with serverless storage on EMR Serverless. We take a deeper look at how serverless storage helps reduce costs for shuffle-heavy Spark workloads, and we outline practical guidance on identifying the types of queries that can benefit most from enabling serverless storage in your EMR Serverless Spark jobs.

In this post, we demonstrate how to improve HBase read performance by implementing bucket caching on Amazon EMR. Our tests reduced latency by 57.9% and improved throughput by 138.8%. This solution is particularly valuable for large-scale HBase deployments on Amazon S3 that need to optimize read performance while managing costs.

This post explores how to build and manage a comprehensive extract, transform, and load (ETL) pipeline using SageMaker Unified Studio workflows through a code-based approach. We demonstrate how to use a single, integrated interface to handle all aspects of data processing, from preparation to orchestration, by using AWS services including Amazon EMR, AWS Glue, Amazon Redshift, and Amazon MWAA. This solution streamlines the data pipeline through a single UI.

In this post, you learn how to build Log Lake, a customizable cross-company data lake for compliance-related use cases that combines AWS CloudTrail and Amazon CloudWatch logs. You’ll discover how to set up separate tables for writing and reading, implement event-driven partition management using AWS Lambda, and transform raw JSON files into read-optimized Apache ORC format using AWS Glue jobs. Additionally, you’ll see how to extend Log Lake by adding Amazon Bedrock model invocation logs to enable human review of agent actions with elevated permissions, and how to use an AI agent to query your log data without writing SQL.

As organizations scale their observability and analytics capabilities across multiple AWS Regions and environments, maintaining consistent dashboards becomes increasingly complex. Teams often spend hours manually recreating dashboards, creating workspaces, linking data sources, and validating configurations across deployments—a repetitive and error-prone process that slows down operational visibility. The next generation OpenSearch UI in Amazon OpenSearch Service […]

Organizations often struggle with building scalable and maintainable data lakes—especially when handling complex data transformations, enforcing data quality, and monitoring compliance with established governance. Traditional approaches typically involve custom scripts and disparate tools, which can increase operational overhead and complicate access control. A scalable, integrated approach is needed to simplify these processes, improve data reliability, […]

In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.

This post shows step-by-step guidance to setup workforce access to Amazon SageMaker Unified Studio using Okta as an external Identity provider with AWS IAM Identity Center.

In this post, we introduce an alternative architecture to synchronize mainframe data to the cloud using Amazon Managed Streaming for Apache Kafka (Amazon MSK) for greater flexibility and scalability. This event-driven approach provides additional possibilities for mainframe data integration and modernization strategies.

Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including […]