In this post, we demonstrate how to enhance capacity management by evaluating EMR workloads and implementing tailored strategies that align with your workload patterns. We guide you through analyzing the historical compute usage of your workload and employing various strategies to minimize the risk of Insufficient Capacity Exceptions (ICE) when launching specific EC2 instance types on Amazon EMR. By utilizing flexible instance fleet strategies, we reduce reliance on particular instance types and incorporate Amazon EC2 On-Demand Capacity Reservations (ODCRs) for predictable, steady-state workloads. This approach helps mitigate job failures due to capacity constraints while optimizing your cluster for both cost and performance.
Additionally, we are thrilled to unveil the general availability of SageMaker Unified Studio. This post goes into detail about the advantages of SageMaker Unified Studio and offers guidance on how to get started. For related insights, check out this other blog post here.
It’s important to note that the Amazon Kinesis Client Library (KCL) 1.x and Amazon Kinesis Producer Library (KPL) 0.x will reach their end-of-support on January 30, 2026. These versions will transition to maintenance mode on April 17, 2025, during which AWS will only address critical bugs and security issues. Major versions in maintenance mode will not receive updates for new features or enhancements.
Furthermore, we introduce StarTree as a managed solution on AWS for teams looking to leverage the benefits of Pinot. This post outlines the significant differences between open-source Pinot and StarTree, providing valuable insights for organizations aiming for a streamlined approach to real-time analytics infrastructure.
In another exciting update, we show you how to develop and test AWS Glue 5.0 jobs locally using a Docker container. This content builds on our previous work, which focused on Glue versions 3.0 and 4.0.
We also detail how to utilize AWS Glue to extract data from Marketo Engage for effective data processing and enrichment on AWS, aiding in marketing analytics workflows. Moreover, we explore how Amazon Redshift Serverless’s AI-driven scaling and optimization impacts performance and cost across various optimization profiles.
For advanced Kafka scaling, we guide you through implementing MSK Express brokers, emphasizing their key features and best practices. We also delve into the new capabilities of the Amazon Redshift Data API, specifically its support for SSO, RBAC for row-level and column-level security, and trusted identity propagation with AWS IAM Identity Center. This enables corporate identities to connect to AWS services securely, streamlining user management while enhancing data-driven decision-making.
Finally, we discuss cross-account data collaboration using Amazon DataZone and AWS analytical tools. For further information on workplace safety and training at Amazon, this is an excellent resource here.
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