Category: AWS Glue

AWS Glue provides a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs. The first post of the series, Best practices to scale Apache Spark jobs and partition data with AWS Glue, discusses best practices to […]

The AWS Cost & Usage Report (CUR) tracks your AWS usage and provides estimated charges associated with that usage. You can configure this report to present the data at hourly or daily intervals, and it is updated at least one time per day until it is finalized at the end of the billing period. The […]

Many AWS customers use a multi-account strategy. A centralized AWS Glue Data Catalog is important to minimize the amount of administration related to sharing metadata across different accounts. This post introduces capability that allows Amazon Athena to query a centralized Data Catalog across different AWS accounts. Overview of solution In late 2019, AWS introduced the […]

This is a guest post by Arvind Godbole, Lead Software Engineer with FactSet and Tarik Makota, AWS Principal Solutions Architect. In their own words “FactSet creates flexible, open data and software solutions for tens of thousands of investment professionals around the world, which provides instant access to financial data and analytics that investors use to […]

This post outlines the approach taken by Intuit, though it is important to remember that there are many ways to build a data lake (for example, AWS Lake Formation). We’ll cover the technologies and processes involved in creating the Intuit Data Lake at a high level, including the overall structure and the automation used in provisioning accounts and resources. Watch this space in the future for more detailed blog posts on specific aspects of the system, from the other teams and engineers who worked together to build the Intuit Data Lake.

The first post of this series discusses two key AWS Glue capabilities to manage the scaling of data processing jobs. The first allows you to horizontally scale out Apache Spark applications for large splittable datasets. The second allows you to vertically scale up memory-intensive Apache Spark applications with the help of new AWS Glue worker types. The post also shows how to use AWS Glue to scale Apache Spark applications with a large number of small files commonly ingested from streaming applications using Amazon Kinesis Data Firehose. Finally, the post shows how AWS Glue jobs can use the partitioning structure for large datasets in Amazon S3 to provide faster execution times for Apache Spark applications.

In this post, I show how to use AWS Step Functions and AWS Glue Python Shell to orchestrate tasks for those Amazon Redshift-based ETL workflows in a completely serverless fashion. AWS Glue Python Shell is a Python runtime environment for running small to medium-sized ETL tasks, such as submitting SQL queries and waiting for a response. Step Functions lets you coordinate multiple AWS services into workflows so you can easily run and monitor a series of ETL tasks. Both AWS Glue Python Shell and Step Functions are serverless, allowing you to automatically run and scale them in response to events you define, rather than requiring you to provision, scale, and manage servers.

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.

A data lake is a centralized store of a variety of data types for analysis by multiple analytics approaches and groups. Many organizations are moving their data into a data lake. In this post, we explore how you can use AWS Lake Formation to build, secure, and manage data lakes.

In this post, I show you how to use AWS Glue to extract data from a Salesforce.com account object and save it to Amazon S3. You then use Amazon Athena to generate a report by joining the account object data from Salesforce.com with the orders data from a separate order management system.