AWS Big Data Blog

Category: Amazon Athena

Query a Teradata database using Amazon Athena Federated Query and join with data in your Amazon S3 data lake

If you use data lakes in Amazon Simple Storage Service (Amazon S3) and use Teradata as your transactional data store, you may need to join the data in your data lake with Teradata in the cloud, Teradata running on Amazon Elastic Compute Cloud (Amazon EC2), or with an on-premises Teradata database, for example to build […]

Query an Apache Hudi dataset in an Amazon S3 data lake with Amazon Athena part 1: Read-optimized queries

July 2023: This post was reviewed for accuracy. On July 16, 2021, Amazon Athena upgraded its Apache Hudi integration with new features and support for Hudi’s latest 0.8.0 release. Hudi is an open-source storage management framework that provides incremental data processing primitives for Hadoop-compatible data lakes. This upgraded integration adds the latest community improvements to […]

Query Snowflake using Athena Federated Query and join with data in your Amazon S3 data lake

This post was last reviewed and updated July, 2022 with updates in Athena federation connector. If you use data lakes in Amazon Simple Storage Service (Amazon S3) and use Snowflake as your data warehouse solution, you may need to join your data in your data lake with Snowflake. For example, you may want to build […]

Query your Oracle database using Athena Federated Query and join with data in your Amazon S3 data lake

This post was last reviewed and updated July, 2022 with updates in Athena federation connector. If you use data lakes in Amazon Simple Storage Service (Amazon S3) and use Oracle as your transactional data store, you may need to join the data in your data lake with Oracle on Amazon Relational Database Service (Amazon RDS), Oracle running on Amazon […]

Create a secure data lake by masking, encrypting data, and enabling fine-grained access with AWS Lake Formation

You can build data lakes with millions of objects on Amazon Simple Storage Service (Amazon S3) and use AWS native analytics and machine learning (ML) services to process, analyze, and extract business insights. You can use a combination of our purpose-built databases and analytics services like Amazon EMR, Amazon OpenSearch Service, and Amazon Redshift as […]

Hydrate your data lake with SaaS application data using Amazon AppFlow

Organizations today want to make data-driven decisions. The data could lie in multiple source systems, such as line of business applications, log files, connected devices, social media, and many more. As organizations adopt software as a service (SaaS) applications, data becomes increasingly fragmented and trapped in different “data islands.” To make decision-making easier, organizations are […]

Build a data quality score card using AWS Glue DataBrew, Amazon Athena, and Amazon QuickSight

Data quality plays an important role while building an extract, transform, and load (ETL) pipeline for sending data to downstream analytical applications and machine learning (ML) models. The analogy “garbage in, garbage out” is apt at describing why it’s important to filter out bad data before further processing. Continuously monitoring data quality and comparing it […]

Speed up your Amazon Athena queries using partition projection

This post is co-written with Steven Wasserman of Vertex, Inc. Amazon Athena is an interactive query service that makes it easy to analyze data stored in Amazon Simple Storage Service (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. […]

How Imperva uses Amazon Athena for machine learning botnets detection

This is a guest post by Ori Nakar, Principal Engineer at Imperva. In their own words, “Imperva is a large cyber security company and an AWS Partner Network (APN) Advanced Technology Partner, who protects web applications and data assets. Imperva protects over 6,200 enterprises worldwide and many of them use Imperva Web Application Firewall (WAF) […]