Category: Intermediate (200)

In this post, we demonstrate a use case where you might need to use an MSK cluster in one AWS account, but MSK Connect is located in a separate account. We demonstrate how to implement IAM authentication after establishing network connectivity. IAM provides enhanced security measures, making sure your systems are protected against unauthorized access.

This post explores how to effectively architect a solution that addresses this specific challenge: enabling comprehensive analytics capabilities for global teams while making sure that your data remains in the AWS Regions required by your compliance framework. We use a variety of AWS services, including Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon QuickSight.

In this post, we demonstrate how you can bring transactional data from AWS OLTP data stores like Amazon Relational Database Service (Amazon RDS) and Amazon Aurora flowing into Redshift using zero-ETL integrations to SageMaker Lakehouse Federated Catalog (Bring your own Amazon Redshift into SageMaker Lakehouse). With this integration, you can now seamlessly onboard the changed data from OLTP systems to a unified lakehouse and expose the same to analytical applications for consumptions using Apache Iceberg APIs from new SageMaker Unified Studio.

This post provides guidance on how you can use event driven architecture to enhance the resiliency of data solutions built on the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI. SageMaker is a managed service with high availability and durability.

In this blog post we shall cover how understanding real-time payout performance, identifying customer behavior patterns across regions, and optimizing internal operations required more than traditional business intelligence and analytics tools. And how since implementing Amazon Redshift and Zero-ETL, MassPay has seen 90% reduction in data availability latency, payments data available for analytics 1.5x faster, leading to 45% reduction in time-to-insight and 37% fewer support tickets related to transaction visibility and payment inquiries.

In this post, we’ll build on the first post in this series to show you how to set up an Apache Iceberg data lake catalog using Amazon S3 Tables and provide different levels of access control to your data. Through this example, you’ll set up fine-grained access controls for multiple users and see how this works using Amazon Redshift. We’ll also review an example with simultaneously using data that resides both in Amazon Redshift and Amazon S3 Tables, enabling a unified analytics experience.

In this post, we explore how LaunchDarkly scaled the internal analytics platform up to 14,000 tasks per day, with minimal increase in costs, after migrating from another vendor-managed Apache Airflow solution to AWS, using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon Elastic Container Service (Amazon ECS).

In this blog post, we tell you how we enhanced the snapshot efficiency in Amazon OpenSearch Service while carefully maintaining these critical operational aspects. These snapshot optimizations are enabled for all OpenSearch optimized instance family (OR1, OR2, OM2) domains from version 2.17 onwards.

This post outlines a solution to automatically replicate the entitlements for readers from the source (AWS Lake Formation) to Amazon QuickSight. This solution can be used even when the authentication method in Amazon QuickSight is not using IAM Identity Center and can work with both direct query and SPICE datasets in Amazon QuickSight.

This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.