AWS Big Data Blog

Today, we’re excited to announce the general availability of the collection groups feature for Amazon OpenSearch Serverless. With this feature you can reduce compute costs for multi-tenant workloads while creating secure tenant boundaries through per-tenant encryption, giving you the flexibility to balance cost efficiency with the exact level of isolation and security your applications requires.

If you’ve ever shopped on Amazon, you’ve used Your Orders. This feature maintains your complete order history dating back to 1995, so you can track and manage every purchase you’ve made. The order history search feature lets you find your past purchases by entering keywords in the search bar. Beyond just finding items, it provides a straightforward way to repurchase the same or similar items, saving you time and effort. In this post, we show you how the Your Orders team improved order history search by introducing semantic search capabilities on top of our existing lexical search system, using Amazon OpenSearch Service and Amazon SageMaker.

Swiss Life Germany, a leading provider of customized pension products with over 100 years of experience, recently transitioned from legacy on-premises infrastructure to a modern cloud architecture. To enable secure data sharing and cross-departmental collaboration in this regulated environment, they implemented Amazon SageMaker with a custom Terraform pattern. This post demonstrates how Swiss Life Germany aligned SageMaker’s agility with their rigorous infrastructure as code standards, providing a blueprint for platform engineers and data architects in highly regulated enterprises.

In this post, we walk through simulating a scenario based on data producer and data consumer that exists before Amazon SageMaker Catalog adoption. We use a sample dataset to simulate existing data and an existing application using an AWS Lambda function, then implement a data mesh pattern using Amazon SageMaker Catalog while keeping your current data repositories and consumer applications unchanged.

CyberArk is a global leader in identity security. Centered on intelligent privilege controls, it provides comprehensive security for human, machine, and AI identities across business applications, distributed workforces, and hybrid cloud environments. In this post, we show you how CyberArk redesigned their support operations by combining Iceberg’s intelligent metadata management with AI-powered automation from Amazon Bedrock. You’ll learn how to simplify data processing flows, automate log parsing for diverse formats, and build autonomous investigation workflows that scale automatically.

In this post, we guide you through the steps to determine if your OpenSearch Service domain is right-sized, using AWS tools and best practices to optimize your configuration for workloads like log analytics, search, vector search, or synthetic data testing.

In this post, you’ll learn how to use Terraform to automate and streamline your Apache Flink application lifecycle management on Amazon Managed Service for Apache Flink. We’ll walk you through the complete lifecycle including deployment, updates, scaling, and troubleshooting common issues. This post builds upon our two-part blog series “Deep dive into the Amazon Managed Service for Apache Flink application lifecycle”.

In this post, we explore how AWS Glue extract, transform, and load (ETL) capabilities connect Google applications and Amazon Redshift, helping you unlock deeper insights and drive data-informed decisions through automated data pipeline management. We walk you through the process of using AWS Glue to integrate data from Google Search Console and write it to Amazon Redshift.

Verisk, a catastrophe modeling SaaS provider serving insurance and reinsurance companies worldwide, cut processing time from hours to minutes-level aggregations while reducing storage costs by implementing a lakehouse architecture with Amazon Redshift and Apache Iceberg. If you’re managing billions of catastrophe modeling records across hurricanes, earthquakes, and wildfires, this approach eliminates the traditional compute-versus-cost trade-off by separating storage from processing power. In this post, we examine Verisk’s lakehouse implementation, focusing on four architectural decisions that delivered measurable improvements.

While general sizing guidelines for OpenSearch Service domains are covered in detail in OpenSearch Service documentation, in this post we specifically focus on T-shirt-sizing OpenSearch Service domains for e-commerce search workloads. T-shirt sizing simplifies complex capacity planning by categorizing workloads into sizes like XS, S, M, L, XL based on key workload parameters such as data volume and query concurrency.