AWS Big Data Blog

In this blog post, we will demonstrate how business units can use Amazon SageMaker Unified Studio to discover, subscribe to, and analyze these distributed data assets. Through this unified query capability, you can create comprehensive insights into customer transaction patterns and purchase behavior for active products without the traditional barriers of data silos or the need to copy data between systems.

Amazon OpenSearch Service also offers a multi-tiered storage solution to its customers in the form of UltraWarm and Cold tiers. In this post, we discuss this new capability and its use cases, and provide a cost-benefit analysis in different scenarios.

This post discusses a decoupled approach of building a serverless data lakehouse using AWS Cloud-centered services, including Amazon EMR Serverless, Amazon Athena, Amazon Simple Storage Service (Amazon S3), Apache DolphinScheduler (an open source data job scheduler) as well as PingCAP TiDB, a third-party data warehouse product that can be deployed either on premises or on the cloud or through a software as a service (SaaS).

Apache HBase is a massively scalable, distributed big data store in the Apache Hadoop ecosystem. We can use Amazon EMR with HBase on top of Amazon Simple Storage Service (Amazon S3) for random, strictly consistent real-time access for tables with Apache Kylin. This post demonstrates how to gracefully decommission target region servers programmatically.

In this post, we show how to optimize capacity by analyzing EMR workloads and implementing strategies tailored to your workload patterns. We walk through assessing the historical compute usage of a workload and use a combination of strategies to reduce the likelihood of InsufficientCapacityExceptions (ICE) when Amazon EMR launches specific EC2 instance types. We implement flexible instance fleet strategies to reduce dependency on specific instance types and use Amazon EC2 On-Demand Capacity Reservation (ODCRs) for predictable, steady-state workloads. Following this approach can help prevent job failures due to capacity limits while optimizing your cluster for cost and performance.

We are excited to announce the general availability of SageMaker Unified Studio. In this post, we explore the benefits of SageMaker Unified Studio and how to get started.

Amazon Kinesis Client Library (KCL) 1.x and Amazon Kinesis Producer Library (KPL) 0.x will reach end-of-support on January 30, 2026. Accordingly, these versions will enter maintenance mode on April 17, 2025. During maintenance mode, AWS will provide updates only for critical bug fixes and security issues. Major versions in maintenance mode will not receive updates for new features or feature enhancements.

In this post, we introduce StarTree as a managed solution on AWS for teams seeking the advantages of Pinot. We highlight the key distinctions between open-source Pinot and StarTree, and provide valuable insights for organizations considering a more streamlined approach to their real-time analytics infrastructure.

In this post, we show how to develop and test AWS Glue 5.0 jobs locally using a Docker container. This post is an updated version of the post Develop and test AWS Glue version 3.0 and 4.0 jobs locally using a Docker container, and uses AWS Glue 5.0.

In this post, we show you how to use AWS Glue to extract data from Marketo Engage for data processing and enrichment on AWS for use in marketing analytics workflows.