Category: AWS re:Invent

ChaosSearch is an Amazon S3-native database built on a serverless, stateless compute architecture within AWS that delivers live search, SQL, and Generative AI analytics. At ChaosSearch, the speed and performance of our architecture is important to us and our customers because time to results is the difference between success and failure, and we rely on […]

Deep learning processes often need to read full datasets, which are usually hundreds of gigabytes in size, before they can perform intelligent data processing. High data retrieval speed and low latency from storage are crucial for enterprises running these performance-critical workloads. Akridata, an AWS independent software vendor (ISV) partner, helps make artificial intelligence (AI)-assisted unstructured-data […]

Machine learning presents a number of new challenges to data teams, calling for technology solutions that can support training and fine-tuning performance-critical workloads with high performance. Data version control is one of the facets of high-performing ML pipelines, as it allows efficient experimentation and full ML pipeline reproducibility at scale. lakeFS by Treeverse, an AWS […]

ClickHouse is a columnar database management system (DBMS) designed for blazing-fast real-time analytics. It was built to address the needs of interactive analytical applications requiring up-to-the-second analytics. To do that, it must support real-time data ingestion at the rate of hundreds of millions of events per second and run complex analytical queries, such as filtering, […]

Organizations are modernizing their data lakes on Amazon Simple Storage Service (Amazon S3) to handle the ever-growing data volume and speed while meeting the demands of analytics, machine learning (ML), artificial intelligence (AI), and generative AI applications. To enable a data-driven culture and remain innovative, the data platform must allow for data-centric collaboration across business […]

This post is Part 2 of a two-part blog post series that will take you, an application developer, through the process of configuring and developing a data application that authenticates users with Microsoft Entra ID and then uses S3 Access Grants to access data on those users’ behalf. Part 1 of this series gave an […]

This is Part 1 of a two-part blog series: Configuring the application. Here is Part 2: Developing the application. When we at AWS talk to our customers about their data lakes, they usually describe a desired access pattern in which users and groups from a corporate directory are granted access to datasets in Amazon Simple […]

Data is at the center of every machine learning pipeline. Whether pre-training foundation models (FMs), fine-tuning FMs with business-specific data, or serving inference queries, every step of the machine learning lifecycle needs low-cost, high-performance data storage to keep compute resources busy and performing useful work. Customers use Amazon Simple Storage Service (Amazon S3) to store training data […]

Amazon Simple Storage Service (Amazon S3) is the most popular object storage platform for modern data lakes. Organizations today evolved to adopt a lake house architecture that combines the scalability and cost effectiveness of data lakes with the performance and ease-of-use of data warehouses. Likewise, Amazon S3 plays an increasingly important role as the foundational […]

To adhere to the principle of least privilege, users define granular access to their Amazon Simple Storage Service (Amazon S3) data based on applications, personas, groups or organization units (OUs). This practice helps customers to mitigate the risk of unauthorized access, limiting potential damage in case of a security breach as employees only have access […]