AWS Big Data Blog

Amazon Q data integration, introduced in January 2024, allows you to use natural language to author extract, transform, load (ETL) jobs and operations in AWS Glue specific data abstraction DynamicFrame. This post introduces exciting new capabilities for Amazon Q data integration that work together to make ETL development more efficient and intuitive. We’ve added support for DataFrame-based code generation that works across any Spark environment. We’ve also introduced in-prompt context-aware development that applies details from your conversations, working seamlessly with a new iterative development experience.

Jumia is a technology company born in 2012, present in 14 African countries, with its main headquarters in Lagos, Nigeria. In this post, we share part of the journey that Jumia took with AWS Professional Services to modernize its data platform that ran under a Hadoop distribution to AWS serverless based solutions.

HEMA is a household Dutch retail brand name since 1926, providing daily convenience products using unique design. This post describes how HEMA used Amazon DataZone to build their data mesh and enable streamlined data access across multiple business areas. It explains HEMA’s unique journey of deploying Amazon DataZone, the key challenges they overcame, and the transformative benefits they have realized since deployment in May 2024. From establishing an enterprise-wide data inventory and improving data discoverability, to enabling decentralized data sharing and governance, Amazon DataZone has been a game changer for HEMA.

In this post, we explore new features of the AWS Glue Data Catalog, which now supports improved automatic compaction of Iceberg tables for streaming data, making it straightforward for you to keep your transactional data lakes consistently performant. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance

In this post, we demonstrate how to implement a custom subscription workflow using Amazon DataZone, Amazon EventBridge, and AWS Lambda to automate the fulfillment process for unmanaged data assets, such as unstructured data stored in Amazon S3. This solution enhances governance and simplifies access to unstructured data assets across the organization.

In this blog post, we’ll dive into the various scenarios for how Cohere Rerank 3.5 improves search results for best matching 25 (BM25), a keyword-based algorithm that performs lexical search, in addition to semantic search. We will also cover how businesses can significantly improve user experience, increase engagement, and ultimately drive better search outcomes by implementing a reranking pipeline.

On November 11, 2024, the Apache Flink community released a new version of AWS services connectors, an AWS open source contribution. This new release, version 5.0.0, introduces a new source connector to read data from Amazon Kinesis Data Streams. In this post, we explain how the new features of this connector can improve performance and reliability of your Apache Flink application.

Amazon Redshift made significant strides in 2024, that enhanced price-performance, enabled data lakehouse architectures by blurring the boundaries between data lakes and data warehouses, simplified ingestion and accelerated near real-time analytics, and incorporated generative AI capabilities to build natural language-based applications and boost user productivity. This blog post provides a comprehensive overview of the major product innovations and enhancements made to Amazon Redshift in 2024.

DeNA Co., Ltd. (DeNA) engages in a variety of businesses, from games and live communities to sports & the community and healthcare & medical, under our mission to delight people beyond their wildest dreams. This post introduces a case study where DeNA combined Amazon Redshift Serverless and dbt (dbt Core) to accelerate data quality tests in their business.

Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.