Category: Amazon Athena

Data is the lifeblood of a digital business and a key competitive advantage for many companies holding large amounts of data in multiple cloud regions. Imperva protects web applications and data assets, and in this post we examine how you can use SQL to analyze big data directly, or to pre-process the data for further analysis by machine learning. You’ll also learn about the benefits and limitations of using SQL, and see examples of clustering and data extraction.

Analyzing large datasets can be challenging, especially if you aren’t thinking about certain characteristics of the data and what you’re ultimately looking to achieve. There are a number of factors organizations need to consider in order to build systems that are flexible, affordable, and fast. Here, experts from CloudZero walk through how to use AWS services to analyze customer billing data and provide value to end users.

Deloitte’s Gary Arora, an APN Ambassador, will show you how to integrate an application with Amazon Athena to execute SQL queries with ‘athena-express.’ This is a wrapper around the AWS SDK that can simplify executing SQL queries in Amazon Athena and fetch the JSON results in the same synchronous call—a capability well suited for many web applications. Developers can use ‘athena-express’ to help save time and effort in setting up the integration and focus on core application development.

Amazon Athena is a Presto-distributed SQL engine used to query unstructured data using standard SQL, where you pay only for the amount of data you query. Chartio, an AWS Competency Partner, recently announced support for Athena. Their solution allows you to query and visualize data stored in an Amazon S3 data lake using standard SQL. You can also use their visual drag-and-drop SQL layer, which generates native SQL queries for you.