Category: Technical How-to

In this post, you learn how to set up Kiro with the Amazon Redshift MCP server to query your data warehouse using natural language. You explore cluster discovery, schema browsing, analytical queries, cross-cluster comparisons, and data quality checks, all without writing SQL from scratch or switching between tools.

In a previous post, we showed you how to query Amazon SageMaker Catalog metadata using SQL by using the metadata export feature. This post builds on that foundation by demonstrating how to create governance dashboards with Amazon Quick.

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.

This post offers an overview of the security mechanisms available for Amazon OpenSearch Service, spanning authentication and authorization, encryption, and network access controls. You learn how to implement fine-grained access control, manage AWS Identity and Access Management (IAM) roles, and secure data both in transit and at rest for both public and virtual private cloud (VPC) access domains.

In this post, you’ll walk through a practical, step-by-step example that shows how to capture and track data lineage from Spark jobs running on Amazon EMR directly into Amazon SageMaker Catalog using OpenLineage. You’ll see how lineage metadata flows automatically and explore data relationships and dependencies across your workflows in Amazon SageMaker Unified Studio.

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.