AWS Big Data Blog

In this post, we highlight key lessons learned while helping a global financial services provider migrate their Apache Hadoop clusters to AWS and best practices that helped reduce their Amazon EMR, Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Storage Service (Amazon S3) costs by over 30% per month.

In this post, we show you how to use AWS Glue Data Quality, a feature of AWS Glue, to establish data parity during data modernization and migration programs with minimal configuration and infrastructure setup. AWS Glue Data Quality enables you to automatically measure and monitor the quality of your data in data repositories and AWS Glue ETL pipelines.

This post demonstrates a method to selectively install Python dependencies based on the Amazon MWAA component type (web server scheduler, or worker) from a Git repository only accessible from your virtual private cloud (VPC).

We recently announced a new capacity level of 30TB for time series data per account per AWS Region. The OpenSearch Serverless compute capacity for data ingestion and search/query is measured in OpenSearch Compute Units (OCUs), which are shared among various collections with the same AWS Key Management Service (AWS KMS) key. This post discusses how you can analyze 30TB time series datasets with OpenSearch Serverless.

This post demonstrates how to implement a dynamic rules engine using Amazon Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.

After careful consideration, we have made the decision to end support for Governed Tables, effective December 31, 2024, to focus on open source transactional table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. This decision stems from customer preference for these open source solutions, which offer ACID-compliant transactions, compaction, time travel, and other features previously provided by Governed Tables.

Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. In this post, we highlight the performance improvements we observed using industry standard TPC-DS benchmarks. Overall execution time of TPC-DS 3 TB benchmark improved by 3x. Some of the queries in our benchmark experienced up to 12x speed up.

We have launched job worker metrics in Amazon CloudWatch for EMR Serverless. This feature allows you to monitor vCPUs, memory, ephemeral storage, and disk I/O allocation and usage metrics at an aggregate worker level for your Spark and Hive jobs. This post is part of a series about EMR Serverless observability. In this post, we discuss how to use these CloudWatch metrics to monitor EMR Serverless workers in near real time.

In this post, we explore a solution using AWS Lake Formation and AWS IAM Identity Center to address the complex challenges of managing and governing legacy data during digital transformation. We demonstrate how enterprises can effectively preserve historical data while enforcing compliance and maintaining user entitlements. This solution enables your organization to maintain robust audit trails, enforce governance controls, and provide secure, role-based access to data.

Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset. This post shows how to integrate Amazon Bedrock with the AWS Serverless Data Analytics Pipeline architecture using Amazon EventBridge, AWS Step Functions, and AWS Lambda to automate a wide range of data enrichment tasks in a cost-effective and scalable manner.