Category: AWS Glue

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.

Building a data lake on Amazon S3 provides an organization with countless benefits. It allows you to access diverse data sources, determine unique relationships, build AI/ML models to provide customized customer experiences, and accelerate the curation of new datasets for consumption. However, capturing and loading continuously changing updates from operational data stores—whether on-premises or on […]

The financial impact of fraud in any industry is massive. According to the Financial Times article Fraud Costs Telecoms Industry $17bn a Year (paid subscription required), fraud costs the telecommunications industry $17 billion in lost revenues every year. Fraudsters constantly look for new technologies and devise new techniques. This changes fraud patterns and makes detection […]

In this post, I show you how to use AWS Glue’s DynamoDB integration and AWS Step Functions to create a workflow to export your DynamoDB tables to S3 in Parquet. I also show how to create an Athena view for each table’s latest snapshot, giving you a consistent view of your DynamoDB table exports.

In Amazon Simple Storage Service (Amazon S3), you can use cross-region replication (CRR) to copy objects automatically and asynchronously across buckets in different AWS Regions. CRR is a bucket-level configuration, and it can help you meet compliance requirements and minimize latency by keeping copies of your data in different Regions. CRR replicates all objects in […]