Category: Amazon Athena

In this post, we show you how to use Spark SQL in Amazon Athena notebooks and work with Iceberg, Hudi, and Delta Lake table formats. We demonstrate common operations such as creating databases and tables, inserting data into the tables, querying data, and looking at snapshots of the tables in Amazon S3 using Spark SQL in Athena.

This post is written in collaboration with Claudia Chitu and Spyridon Dosis from ACAST. Founded in 2014, Acast is the world’s leading independent podcast company, elevating podcast creators and podcast advertisers for the ultimate listening experience. By championing an independent and open ecosystem for podcasting, Acast aims to fuel podcasting with the tools and monetization needed […]

As data engineering becomes increasingly complex, organizations are looking for new ways to streamline their data processing workflows. Many data engineers today use Apache Airflow to build, schedule, and monitor their data pipelines. However, as the volume of data grows, managing and scaling these pipelines can become a daunting task. Amazon Managed Workflows for Apache […]

Today, we’re making available a new capability of AWS Glue Data Catalog that allows generating column-level statistics for AWS Glue tables. These statistics are now integrated with the cost-based optimizers (CBO) of Amazon Athena and Amazon Redshift Spectrum, resulting in improved query performance and potential cost savings. Data lakes are designed for storing vast amounts […]

Amazon Athena is a serverless, interactive analytics service built on open source frameworks, supporting open table file formats. Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises […]

Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale. DynamoDB offers built-in security, continuous backups, automated multi-Region replication, in-memory caching, and data import and export tools. The scalability and flexible data schema of DynamoDB make it well-suited for a variety of use cases. These include internet-scale […]

This post is written in collaboration with Philipp Karg and Alex Gutfreund  from BMW Group. Bayerische Motoren Werke AG (BMW) is a motor vehicle manufacturer headquartered in Germany with 149,475 employees worldwide and the profit before tax in the financial year 2022 was € 23.5 billion on revenues amounting to € 142.6 billion. BMW Group is one of the […]

No matter the industry or level of maturity within AWS, our customers require better visibility into their AWS Glue usage. Better visibility can lend itself to gains in operational efficiency, informed business decisions, and further transparency into your return on investment (ROI) when using the various features available through AWS Glue. As your company grows, […]

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting […]

In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.