Category: Analytics

HAQM Redshift now provides the ability to incrementally refresh your materialized views on data lake tables including open file and table formats such as Apache Iceberg. In this post, we will show you step-by-step what operations are supported on both open file formats and transactional data lake tables to enable incremental refresh of the materialized view.

Today, we’re announcing timelines for end of Standard Support and Extended Support for legacy Elasticsearch versions up to 6.7, Elasticsearch versions 7.1 through 7.8, OpenSearch versions from 1.0 through 1.2, and OpenSearch versions 2.3 through 2.9 available on HAQM OpenSearch Service.

In this post, we show you how to enable the HAQM Q generative SQL feature in the Redshift query editor and use the feature to get tailored SQL commands based on your natural language queries. With HAQM Q, you can spend less time worrying about the nuances of SQL syntax and optimizations, allowing you to concentrate your efforts on extracting invaluable business insights from your data.

HAQM OpenSearch Service launches a modernized operational analytics experience that can provide comprehensive observability spanning multiple data sources, so that you can gain insights from OpenSearch and other integrated data sources in one place. The launch also introduces OpenSearch Workspaces that provides tailored experience for popular use cases and supports access control, so that you can create a private space for your use case and share it only to your collaborators.

This post explores how you can use BladeBridge, a leading data environment modernization solution, to simplify and accelerate the migration of SQL code from BigQuery to HAQM Redshift. BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable HAQM Redshift data warehouse.

We’re introducing a real-time vector embedding blueprint, which simplifies building real-time AI applications by automatically generating vector embeddings using HAQM Bedrock from streaming data in HAQM Managed Streaming for Apache Kafka (HAQM MSK) and indexing them in HAQM OpenSearch Service. In this post, we discuss the importance of real-time data for generative AI applications, typical architectural patterns for building Retrieval Augmented Generation (RAG) capabilities, and how to use real-time vector embedding blueprints for HAQM MSK to simplify your RAG architecture.

We are excited to launch Kinesis Client Library 3.0, which enables you to reduce your stream processing cost by up to 33% compared to previous KCL versions. KCL 3.0 achieves this with a new load balancing algorithm that continuously monitors the resource utilization of workers and redistributes the load evenly to all workers. In this post, we discuss load balancing challenges in stream processing using a sample workload, demonstrating how uneven load distribution across workers increases processing costs.

In this post, we discuss how you can send real-time data streams into Iceberg tables on HAQM S3 by using HAQM Data Firehose. HAQM Data Firehose simplifies the process of streaming data by allowing users to configure a delivery stream, select a data source, and set Iceberg tables as the destination. Once set up, the Firehose stream is ready to deliver data.

In this post, we discuss how to implement fine-grained access control in EMR Serverless using Lake Formation. With this integration, organizations can achieve better scalability, flexibility, and cost-efficiency in their data operations, ultimately driving more value from their data assets.

HAQM Redshift has enhanced its Redshift ML feature to support integration of large language models (LLMs). As part of these enhancements, Redshift now enables native integration with HAQM Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in HAQM Redshift, helping you to build generative AI applications quickly. This powerful combination enables customers to harness the transformative capabilities of LLMs and seamlessly incorporate them into their analytical workflows.