Category: Analytics

In this post, we show you how to enable the Amazon 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 Amazon 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.

Amazon 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 Amazon 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 Amazon 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 Amazon Bedrock from streaming data in Amazon Managed Streaming for Apache Kafka (Amazon MSK) and indexing them in Amazon 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 Amazon 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 Amazon S3 by using Amazon Data Firehose. Amazon 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.

Amazon 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 Amazon Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in Amazon 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.

In this post, we discuss how Volkswagen Autoeuropa used Amazon DataZone to build a data marketplace based on data mesh architecture to accelerate their digital transformation. The data mesh, built on Amazon DataZone, simplified data access, improved data quality, and established governance at scale to power analytics, reporting, AI, and machine learning (ML) use cases. As a result, the data solution offers benefits such as faster access to data, expeditious decision making, accelerated time to value for use cases, and enhanced data governance.

Amazon DataZone recently announced the expansion of data analysis and visualization options for your project-subscribed data within Amazon DataZone using the Amazon Athena JDBC driver. In this post, you learn how the recent enhancements in Amazon DataZone facilitate a seamless connection with Tableau. By integrating Tableau with the comprehensive data governance capabilities of Amazon DataZone, we’re empowering data consumers to quickly and seamlessly explore and analyze their governed data.