AWS Big Data Blog

In this post, we explain how ultrafast delivery pioneer, Getir, unleashed the power of data democratization on a large scale through their data mesh architecture using Amazon Redshift. We start by introducing Getir and their vision—to seamlessly, securely, and efficiently share business data across different teams within the organization for BI, extract, transform, and load (ETL), and other use cases. We’ll then explore how Amazon Redshift data sharing powered the data mesh architecture that allowed Getir to achieve this transformative vision.

Apache HBase is an open source, non-relational distributed database developed as part of the Apache Software Foundation’s Hadoop project. HBase can run on Hadoop Distributed File System (HDFS) or Amazon Simple Storage Service (Amazon S3), and can host very large tables with billions of rows and millions of columns. The followings are some typical use […]

In this post, we’ll explore Infor’s journey to modernize its search capabilities, the key benefits they achieved, and the technologies that powered this transformation. We’ll also discuss how Infor’s customers are now able to more effectively search through business messages, documents, and other critical data within the ION OneView platform.

Adoption of data lakes and the data mesh framework emerges as a powerful approach. By decentralizing data ownership and distribution, enterprises can break down silos and enable seamless data sharing. In this post, we discuss how to choose the right tool for building an enterprise data platform and enabling data sharing, collaboration and access within your organization and with third-party providers. We address three business use cases using AWS Glue, AWS Data Exchange, AWS Clean Rooms, and Amazon DataZone through three different use cases.

In this post, we share the journey of a multi-national financial credit reporting company, including the hurdles they faced, and why they went with Amazon OpenSearch Ingestion pipelines to make their log management smoother.

In this post, we walk you through how to configure service provider-initiated authentication for OpenSearch Dashboards by using OpenSearch Service and Keycloak. We also discuss how to set up users, groups, and roles in Keycloak and configure their access to OpenSearch Dashboards.

We’re excited to announce the general availability (GA) of Amazon DynamoDB zero-ETL integration with Amazon Redshift, which enables you to run high-performance analytics on your DynamoDB data in Amazon Redshift with little to no impact on production workloads running on DynamoDB. As data is written into a DynamoDB table, it’s seamlessly made available in Amazon Redshift, eliminating the need to build and maintain complex data pipelines.

We’re thrilled to announce the launch of the official Amazon OpenSearch Service YouTube channel—a comprehensive resource for anyone looking to master Amazon OpenSearch Service. Whether you’re just getting started with searches , vectors, analytics, or you’re looking to optimize large-scale implementations, our channel can be your go-to resource to help you unlock the full potential of OpenSearch Service.

Amazon Kinesis Data Analytics for SQL is a data stream processing engine that helps you run your own SQL code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics. AWS has made the decision to discontinue Kinesis Data Analytics for SQL, effective January 27, 2026. In this post, we explain why we plan to end support for Kinesis Data Analytics for SQL, alternative AWS offerings, and how to migrate your SQL queries and workloads.

In this post, we demonstrate the critical role of metadata in text-to-SQL generation through an example implemented for Amazon Athena using Amazon Bedrock. We discuss the challenges in maintaining the metadata as well as ways to overcome those challenges and enrich the metadata.