Category: Advanced (300)

Processing encrypted data in Amazon S3 with Amazon EMR and Apache Spark can drive up AWS KMS decrypt API costs as the number of objects grows. This post shows three techniques to reduce those costs without compromising encryption: optimizing file formats (including Apache Iceberg), aggregating data, and using AWS Glue Data Catalog partition indexes.

Learn how to automate Apache Spark 3.x to 4.0 Scala migration on Amazon EMR using the AWS Spark Upgrade Agent. This post covers API deprecations, behavioral changes, build configuration updates, and job validation, turning months of manual effort into hours.

Learn how the Everyday Essentials team built a scalable personalized recommendation platform on AWS using a batch-first architecture with Amazon MWAA for orchestration, Amazon SageMaker for training and vector search, and AWS Lake Formation for governed data access, then extended it to real-time with Amazon MemoryDB.

This post shows you how to use the Catalog objects API CreateTable() to programmatically create ATHENA and SPARK dialects using cross-account IAM definer roles, and how to add the ATHENA dialect programmatically for the views that were created earlier with only SPARK dialect.

In this post, we show you how to implement data streams with Index State Management (ISM) in Amazon OpenSearch Service. This approach automatically manages your time series data lifecycle and optimizes both performance and costs. Data streams distribute incoming data across multiple backing indices, helping to reduce single-index bottlenecks, while ISM policies automate rollover, retention, and storage tiering to help manage costs.

In this post, we show you how to use Amazon SageMaker Unified Studio to implement cross-account data sharing in Amazon Redshift using data mesh principles. We demonstrate how to build a scalable data mesh architecture that supports secure, auditable data sharing across AWS accounts while reducing operational burden.

In this post, we demonstrate an automated test suite that validates your Amazon Redshift cluster automatically after any patch, reboot, or modification. It uses standard drivers against real workload patterns to provide a verified gate between a patch landing and that patch reaching production.

This post focuses on explaining the architecture approach to build the open lakehouse architecture on AWS, unifying the metadata catalog across providers for the AI agents to access. In addition, it highlights the architecture trade-offs and best practices.

In this post, I show you how writable warm storage removes the costly migration cycle. You can reduce your infrastructure costs by up to 48 percent and update historical data in seconds instead of hours. I walk through a real-world cost comparison and performance benchmarks, and help you decide when to use writable warm versus UltraWarm.

In this post, you learn how to build an application log pipeline for production use with Amazon CloudWatch Logs, AWS Lambda, Amazon Data Firehose, AWS Glue, and Apache Iceberg materialized tables. You then use materialized views to accelerate query performance. This solution helps you achieve faster query response times on large-scale log data without requiring you to manage continuous data lake refresh.