Category: Learning Levels

Creating a new cluster with Express brokers is straightforward, as described in Amazon MSK Express brokers. However, if you have an existing MSK cluster, you need to migrate to a new Express based cluster. In this post, we discuss how you should plan and perform the migration to Express brokers for your existing MSK workloads on Standard brokers. Express brokers offer a different user experience and a different shared responsibility boundary, so using them on an existing cluster is not possible. However, you can use Amazon MSK Replicator to copy all data and metadata from your existing MSK cluster to a new cluster comprising of Express brokers.

In this post, we demonstrate how to use the OpenSearch Ingestion’s Lambda processor to generate embeddings for your source data and ingest them to an OpenSearch Serverless vector collection. This solution uses the flexibility of OpenSearch Ingestion pipelines with a Lambda processor to dynamically generate embeddings.

In this post, we address common challenges associated with manual MSK topic configuration management and present a robust Terraform-based solution. This solution supports both provisioned and serverless MSK clusters.

The Amazon EMR runtime for Apache Spark offers a high-performance runtime environment while maintaining 100% API compatibility with open source Apache Spark and Apache Iceberg table format. In this post, we demonstrate the performance benefits of using the Amazon EMR 7.5 runtime for Spark and Iceberg compared to open source Spark 3.5.3 with Iceberg 1.6.1 tables on the TPC-DS 3TB benchmark v2.13.

In this post, we explore new features of the AWS Glue Data Catalog, which now supports improved automatic compaction of Iceberg tables for streaming data, making it straightforward for you to keep your transactional data lakes consistently performant. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance

In this post, we demonstrate how to implement a custom subscription workflow using Amazon DataZone, Amazon EventBridge, and AWS Lambda to automate the fulfillment process for unmanaged data assets, such as unstructured data stored in Amazon S3. This solution enhances governance and simplifies access to unstructured data assets across the organization.

In this post, we explore the process of federating into AWS using Microsoft Entra ID and AWS Identity and Access Management (IAM), and how to restrict access to datasets based on permissions linked to AD groups. We guide you through the setup process, and demonstrate how to seamlessly connect to the Redshift Query Editor while making sure data access permissions are accurately enforced based on your Microsoft Entra ID groups.

This post introduces the new HubSpot managed connector for AWS Glue, and demonstrates how you can integrate HubSpot data into your existing data lake on AWS. By consolidating HubSpot data with data from your AWS accounts and from other SaaS services, you can enhance, analyze, and optionally write the data back to HubSpot, creating a seamless and integrated data experience.

Now, we are announcing general availability (GA) of Amazon Redshift multi-data warehouse writes through data sharing. This new capability allows you to scale your write workloads and achieve better performance for extract, transform, and load (ETL) workloads by using different warehouses of different types and sizes based on your workload needs.

In this post, we explore how to use Aurora MySQL-Compatible Edition Zero-ETL integration with Amazon Redshift and dbt Cloud to enable near real-time analytics. By using dbt Cloud for data transformation, data teams can focus on writing business rules to drive insights from their transaction data to respond effectively to critical, time sensitive events.