AWS Big Data Blog

Category: Expert (400)

Scale fine-grained permissions across warehouses with Amazon Redshift and AWS IAM Identity Center

This post provides a comprehensive technical walkthrough for implementing Amazon Redshift federated permissions with AWS IAM Identity Center to help achieve scalable data governance across multiple data warehouses. It demonstrates a practical architecture where an Enterprise Data Warehouse (EDW) serves as the producer data warehouse with centralized policy definitions, helping automatically enforce security policies to consuming Sales and Marketing data warehouses without manual reconfiguration.

Reducing costs for shuffle-heavy Apache Spark workloads with serverless storage for Amazon EMR Serverless

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.

Optimize HBase reads with bucket caching on Amazon EMR

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 architecture diagram illustrates a comprehensive, end-to-end data processing pipeline built on AWS services, orchestrated through Amazon SageMaker Unified Studio. The pipeline demonstrates best practices for data ingestion, transformation, quality validation, advanced processing, and analytics.

Orchestrate end-to-end scalable ETL pipeline with Amazon SageMaker workflows

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.

Create a customizable cross-company log lake, Part II: Build and add Amazon Bedrock

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.

Managing Amazon OpenSearch UI infrastructure as code with AWS CDK

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 […]

Building scalable AWS Lake Formation governed data lakes with dbt and Amazon Managed Workflows for Apache Airflow

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, […]

Cross-account lakehouse governance with Amazon S3 Tables and SageMaker Catalog

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.

Stream mainframe data to AWS in near real time with Precisely and Amazon MSK

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.