Category: Amazon Athena

Many organizations around the world rely on the use of physical assets, such as vehicles, to deliver a service to their end-customers. By tracking these assets in real time and storing the results, asset owners can derive valuable insights on how their assets are being used to continuously deliver business improvements and plan for future […]

Customers today may struggle to implement proper access controls and auditing at the user level when multiple applications are involved in data access workflows. The key challenge is to implement proper least-privilege access controls based on user identity when one application accesses data on behalf of the user in another application. It forces you to […]

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. However, altering schema and table partitions in traditional data lakes can be a disruptive and time-consuming task, requiring renaming or recreating entire tables and reprocessing large datasets. […]

Public health organizations need access to data insights that they can quickly act upon, especially in times of health emergencies, when data needs to be updated multiple times daily. For example, during the COVID-19 pandemic, access to timely data insights was critically important for public health agencies worldwide as they coordinated emergency response efforts. Up-to-date […]

This post is cowritten with Pramod Nayak, LakshmiKanth Mannem and Vivek Aggarwal from the Low Latency Group of LSEG. Transaction cost analysis (TCA) is widely used by traders, portfolio managers, and brokers for pre-trade and post-trade analysis, and helps them measure and optimize transaction costs and the effectiveness of their trading strategies. In this post, […]

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 […]