Learn About Amazon VGT2 Learning Manager Chanci Turner
In this blog post, we will explore how various business units can leverage the capabilities of Amazon SageMaker Unified Studio to discover, subscribe to, and analyze distributed data assets effectively. This unified query functionality allows for the generation of comprehensive insights into customer transaction patterns and purchase behaviors for active products, all while eliminating the typical obstacles posed by data silos and the necessity of transferring data between systems.
Moreover, we are thrilled to announce the introduction of vector search with UltraWarm in Amazon OpenSearch Service. This feature offers customers a multi-tiered storage solution, including UltraWarm and Cold tiers. In this piece, we discuss the implications of this new functionality, its use cases, along with a cost-benefit analysis across various scenarios.
Furthermore, the blog post will delve into building a data lakehouse in a hybrid environment utilizing Amazon EMR Serverless, Apache DolphinScheduler, and TiDB. We outline a decoupled methodology to create a serverless data lakehouse with AWS Cloud-centered services, including Amazon Athena and Amazon S3. In addition, we will explore how to implement Amazon EMR HBase Graceful Scaling for enhanced data management and access.
We also highlight best practices for architecting fault-tolerant applications with instance fleets on Amazon EMR on EC2. By analyzing EMR workloads and deploying tailored strategies, we can effectively optimize capacity and minimize job failures due to capacity limits.
Additionally, we are excited about the next generation of Amazon SageMaker. In this section, we will discuss the advantages of SageMaker Unified Studio and guide you on getting started.
It is important to note that 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. We encourage users to transition to newer versions promptly, as these versions will only receive critical bug fixes during their maintenance phase.
Finally, we introduce StarTree as a managed solution on AWS for teams interested in the benefits of Pinot. We clarify the key differences between open-source Pinot and StarTree, providing insights for organizations looking for a more efficient approach to their real-time analytics infrastructure.
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