Category: Advanced (300)

In this post, we walk through a hands-on PySpark migration from Spark 3.5 to Spark 4.0 on Amazon EMR Serverless, using the AWS Spark Upgrade Agent. You’ll see how the agent iteratively validates your application on a live Amazon EMR Serverless application, automatically diagnosing and resolving failures from Amazon CloudWatch logs until the job succeeds.

This post shows you how to migrate your JMS applications and walks through a complete setup, from creating the broker to sending and receiving messages. You will also see a real-world scenario: migrating an existing Apache ActiveMQ workload to an Amazon MQ broker running RabbitMQ. The post covers configuration changes, monitoring with Amazon CloudWatch, and validation steps to make sure that your migration succeeds.

In this post, you learn how to use Data Agent in Query Editor to explore data, build multi-step analyses, recover from errors, and summarize results using a public education dataset.

In this post, we walk you through the new scheduling and orchestrating capabilities for notebooks in Amazon SageMaker Unified Studio.

In this post, we walk through how Zynga adopted Amazon Redshift federated permissions and AWS IAM Identity Center to enforce consistent, tiered data access across provisioned and serverless Amazon Redshift environments without building custom synchronization pipelines.

In this post, we show you how to tackle data discovery, classification, and governance across your databases, data warehouses, and object storage to regain visibility and control over your data landscape.

Selecting the right SQL processing solution for large-scale data analytics is a critical decision for organizations. As data volumes grow exponentially, the technology landscape has evolved to offer diverse options for processing and analyzing this information efficiently. This post presents a systematic framework for evaluating and benchmarking SQL processing engines on AWS, using Apache JMeter to conduct practical performance testing at scale.

As data volumes grow from terabytes to petabytes, the architecture for generating synthetic data must evolve to meet increasing demands for scale, performance, and data quality. In this post, we show how you can build a scalable synthetic data generation solution using Amazon EMR, Apache Spark, and the Faker library.

In this post, we show you a reference architecture that automates sensitive data discovery across legal document repositories on Amazon Web Services (AWS), demonstrate how to capture structured findings as a compliance dataset, and guide you through building a governed analytics workspace that maintains your security boundaries. You walk away with a practical model for building security and analytics into the same lifecycle, without moving documents outside their system of record.

In this post, you learn how to replicate Amazon DynamoDB data to Apache Iceberg tables in Amazon S3 through a zero-ETL integration. We walk through the challenges that the DynamoDB nested, schema-flexible data model introduces for analytics workloads, and show you how to configure schema unnesting and data partitioning for a sample product catalog table. We also cover how to query the replicated data in Amazon Athena using standard SQL.