Category: Advanced (300)

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.

This post shows how developers, data engineers, and analysts can connect to a secure Spark Connect endpoint in Athena with Apache Spark. You can use your preferred tools, such as Jupyter notebooks, VS Code, or dbt with Apache Airflow, without managing cluster lifecycle or scaling.

We’re introducing a purpose-built log analytics engine for Amazon OpenSearch Service. This new engine delivers up to 4x price performance, 2x faster data ingestion, up to 2x faster analytical queries, and up to 70 percent lower storage costs. You get all of this without sacrificing search capabilities on the same data. In this post, you learn how to take advantage of these benefits, see how to get started, and review benchmark results at billion-document scale.

In this post, you learn how to build an AI-powered solution that collects the telemetry, pre-computes performance signals, correlates them with CloudWatch, and uses Amazon Bedrock to generate prioritized recommendations.