Category: Learning Levels

This post demonstrates how to configure SageMaker Unified Studio with SSO, set up projects and user onboarding, and access data securely using integrated analytics tools.

In this post, we introduce an alternative architecture to synchronize mainframe data to the cloud using Amazon Managed Streaming for Apache Kafka (Amazon MSK) for greater flexibility and scalability. This event-driven approach provides additional possibilities for mainframe data integration and modernization strategies.

As analytical demands grow, many customers are upgrading from DC2 to RA3 or Amazon Redshift Serverless, which offer independent compute and storage scaling, along with advanced capabilities such as data sharing, zero-ETL integration, and built-in artificial intelligence and machine learning (AI/ML) support with Amazon Redshift ML. This post provides a practical guide to plan your target architecture and migration strategy, covering upgrade options, key considerations, and best practices to facilitate a successful and seamless transition.

Amazon SageMaker offers a comprehensive hub that integrates data, analytics, and AI capabilities, providing a unified experience for users to access and work with their data. Through Amazon SageMaker Unified Studio, a single and unified environment, you can use a wide range of tools and features to support your data and AI development needs, including […]

In this post, we demonstrate how to build a serverless architecture that processes real-time ICU patient monitoring data using Lambda event source mapping for immediate alert generation and data aggregation, followed by persistent storage in Amazon S3 with an Iceberg catalog for comprehensive healthcare analytics.

Apache Airflow 3.x on Amazon MWAA introduces architectural improvements such as API-based task execution that provides enhanced security and isolation. This migration presents an opportunity to embrace next-generation workflow orchestration capabilities while providing business continuity. This post provides best practices and a streamlined approach to successfully navigate this critical migration, providing minimal disruption to your mission-critical data pipelines while maximizing the enhanced capabilities of Airflow 3.

In this post, we introduce Amazon DataZone and explore how Volkswagen used Amazon DataZone to build their data mesh, tackle the challenges encountered, and break the data silos.

At Vanguard, we faced significant challenges with our legacy mainframe system that limited our ability to deliver modern, personalized customer experiences. Our centralized database architecture created performance bottlenecks and made it difficult to scale services independently for our millions of personal and institutional investors. In this post, we show you how we modernized our data architecture using Amazon Redshift as our Operational Read-only Data Store (ORDS).

Migrating from Google Cloud’s BigQuery to ClickHouse Cloud on AWS allows businesses to leverage the speed and efficiency of ClickHouse for real-time analytics while benefiting from AWS’s scalable and secure environment. This article provides a comprehensive guide to executing a direct data migration using AWS Glue ETL, highlighting the advantages and best practices for a […]

Laravel, one of the world’s most popular web frameworks, launched its first-party observability platform, Laravel Nightwatch, to provide developers with real-time insights into application performance. Built entirely on AWS managed services and ClickHouse Cloud, the service already processes over one billion events per day while maintaining sub-second query latency, giving developers instant visibility into the health of their applications.