Category: AWS Step Functions

General Data Protection Regulation (GDPR) is an important aspect of today’s technology world, and processing data in compliance with GDPR is a necessity for those who implement solutions within the AWS public cloud. One article of GDPR is the “right to erasure” or “right to be forgotten” which may require you to implement a solution […]

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.

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 this post, I show you how to use AWS Step Functions and AWS Lambda for orchestrating multiple ETL jobs involving a diverse set of technologies in an arbitrarily-complex ETL workflow.

In this post, I’ll show you how to use AWS Step Functions to orchestrate your Spark jobs that are running on Amazon EMR.

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds […]