AWS Big Data Blog

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.

Amazon OpenSearch Service recently introduced a new Transport Layer Security (TLS) policy Policy-Min-TLS-1-2-PFS-2023-10, which supports the latest TLS 1.3 protocol and TLS 1.2 with Perfect Forward Secrecy (PFS) cipher suites. This new policy improves security and enhances OpenSearch performance. In this post, we discuss the benefits of this new policy and how to enable it using the AWS Command Line Interface (AWS CLI).

In this post, we describe Nexthink’s journey as they implemented a new real-time alerting system using Amazon Managed Service for Apache Flink. We explore the architecture, the rationale behind key technology choices, and the Amazon Web Services (AWS) services that enabled a scalable and efficient solution.

As organizations scale their use of OpenSearch Serverless, understanding network architecture and DNS management becomes increasingly important. This post covers advanced deployment scenarios focused on centralized and distributed access patterns—specifically, how enterprises can simplify network connectivity across multiple AWS accounts and extend access to on-premises environments for their OpenSearch Serverless deployments.

At AWS re:Invent 2024, we introduced a no code zero-ETL integration between Amazon DynamoDB and Amazon SageMaker Lakehouse, simplifying how organizations handle data analytics and AI workflows. In this post, we share how to set up this zero-ETL integration from DynamoDB to your SageMaker Lakehouse environment.

In this post, we guide you through the process of creating a Data Catalog view using EMR Serverless, adding the SQL dialect to the view for Athena, sharing it with another account using LF-Tags, and then querying the view in the recipient account using a separate EMR Serverless workspace and AWS Glue 5.0 Spark job and Athena. This demonstration showcases the versatility and cross-account capabilities of Data Catalog views and access through various AWS analytics services.

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.

We’re thrilled to introduce managed query results, a new Athena feature that automatically stores, secures, and manages the lifecycle of query result data for you at no additional cost. In this post, we demonstrate how to get started with managed query results and, by removing the undifferentiated effort spent on query result management, how Athena helps you get insights from your data in fewer steps than before.

This post demonstrates how to centralize Apache Spark observability using SHS on EMR on EKS. We showcase how to enhance SHS with performance monitoring tools, with a pattern applicable to many monitoring solutions such as SparkMeasure and DataFlint.

In this post, we will show you five Amazon Redshift architecture patterns that you can consider to optimize your Amazon Redshift data warehouse performance at scale using features such as Amazon Redshift Serverless, Amazon Redshift data sharing, Amazon Redshift Spectrum, zero-ETL integrations, and Amazon Redshift streaming ingestion.