Category: Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze the data stored in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. You can reduce your per-query costs and get better performance by compressing, partitioning, […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. This post details the two new preview features that you can start using today: connecting […]

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Amazon Athena has announced a public preview of a new feature that provides an easy […]

Organizations today use data stores that are the best fit for the applications they build. For example, for an organization building a social network, a graph database such as Amazon Neptune is likely the best fit when compared to a relational database. Similarly, for workloads that require flexible schema for fast iterations, Amazon DocumentDB (with […]

Amazon Athena recently added support for federated queries and user-defined functions (UDFs), both in Preview. See Query any data source with Amazon Athena’s new federated query for more details. Jornaya helps marketers intelligently connect consumers who are in the market for major life purchases such as homes, mortgages, cars, insurance, and education. Jornaya collects data […]

his post shows how to access and manage data in multiple accounts from a central AWS Lake Formation account. The walkthrough demonstrates a centralized catalog residing in the master Lake Formation account, with data residing in the different accounts. The post shows how to grant access permissions from the Lake Formation service to read, write and update the catalog and access data in different accounts.

This post shows how ironSource uses Upsolver to build, manage, and orchestrate its data lake with minimal coding and maintenance. We discuss why ironSource opted for a data lake architecture based on Amazon S3, how ironSource built the data lake using Upsolver, how to create outputs to analytic services such as Amazon Athena, Amazon ES, and Tablea, and the benefits of this solution.

In this post, we present a solution for analyzing Google Analytics data using Amazon Athena. We’re including a reference architecture built on moving hit-level data from Google Analytics to Amazon S3, performing joins and enrichments, and visualizing the data using Amazon Athena and Amazon QuickSight. Upsolver is used for data lake automation and orchestration, enabling customers to get started quickly.

This post describes how you can improve performance and reduce costs by offloading reporting workloads from an online transaction processing (OLTP) database to Amazon Athena and Amazon S3. The architecture described allows you to implement a reporting system and have an understanding of the data that you receive by being able to query it on arrival.

This post describes how to make the MIMIC-III dataset available in Athena and provide automated access to an analysis environment for MIMIC-III on AWS. We also compare a MIMIC-III reference bioinformatics study using a traditional database to that same study using Athena.