AWS Big Data Blog
Develop and monitor a Spark application using existing data in Amazon S3 with Amazon SageMaker Unified Studio
In this post, we demonstrate how to develop and monitor a Spark application using existing data in Amazon S3 using SageMaker Unified Studio. The solution addresses key challenges organizations face in managing big data analytics workloads through an integrated development environment where data teams can develop, test, and refine Spark applications while leveraging EMR Serverless for dynamic resource allocation and built-in monitoring tools.
Perform per-project cost allocation in Amazon SageMaker Unified Studio
Amazon SageMaker Unified Studio enables per-project cost allocation through resource tagging, allowing organizations to track and manage costs across different projects and domains effectively. This post demonstrates how to implement cost tracking using AWS Billing and Cost Management tools, including Cost Explorer and Data Exports, to help finance and business analysts follow FinOps best practices for controlling cloud infrastructure costs.
Near real-time baggage operational insights for airlines using Amazon Kinesis Data Streams
This post explores a framework developed by IBM to modernize baggage analytics using AWS managed services like Amazon Kinesis Data Streams, DynamoDB Streams, and other AWS services within a serverless architecture. The solution enables near real-time baggage operational insights for airlines, delivering cost savings, enhanced scalability, and improved performance while providing better security and operational efficiency to meet evolving airline needs.
Overcome your Kafka Connect challenges with Amazon Data Firehose
We’re happy to announce a new feature in the Amazon Data Firehose integration with Amazon MSK. You can now specify the Firehose stream to either read from the earliest position on the Kafka topic or from a custom timestamp to begin reading from your MSK topic. In this post of this series, we focus on managed data delivery from Kafka to your data lake.
How Stifel built a modern data platform using AWS Glue and an event-driven domain architecture
In this post, we show you how Stifel implemented a modern data platform using AWS services and open data standards, building an event-driven architecture for domain data products while centralizing the metadata to facilitate discovery and sharing of data products.
Build conversational AI search with Amazon OpenSearch Service
Amazon OpenSearch Service is a versatile search and analytics tool. In this post, we explore conversational search, its architecture, and various ways to implement it.
Enhance stability with dedicated cluster manager nodes using Amazon OpenSearch Service
In this post, we show how to enhance the stability of your OpenSearch Service domain with dedicated cluster manager nodes and how using these in deployment enhances your cluster’s stability and reliability.
Kaltura reduces observability operational costs by 60% with Amazon OpenSearch Service
In this post, we share how Kaltura transformed its observability strategy and technological stack by migrating from a software as a service (SaaS) logging solution to Amazon OpenSearch Service—achieving higher log retention, a 60% reduction in cost, and a centralized platform that empowers multiple teams with real-time insights.
Introducing GenAI-powered business description recommendations for custom assets in Amazon SageMaker Catalog
Amazon SageMaker Catalog now supports generative AI-powered recommendations for business descriptions, including table summaries, use cases, and column-level descriptions for custom structured assets registered programmatically. In this post, we demonstrate how to generate AI recommendations for business descriptions for custom structured assets in SageMaker Catalog.
Amazon Redshift Python user-defined functions will reach end of support after June 30, 2026
The Amazon Redshift integration with AWS Lambda provides the capability to create Amazon Redshift Lambda user-defined functions (UDFs). Because Lambda UDFs provide these significant advantages in integration, flexibility, scalability, and security, we will be ending support for Python UDFs in Amazon Redshift. In this post, we walk you through how to migrate your existing Python UDFs to Lambda UDFs, set up monitoring and cost evaluations, and review key considerations for a smooth transition.