AWS Glue vs Mara: What are the differences?
Developers describe AWS Glue as "Fully managed extract, transform, and load (ETL) service". A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. On the other hand, Mara is detailed as "A lightweight ETL framework". A lightweight ETL framework with a focus on transparency and complexity reduction.
AWS Glue and Mara can be primarily classified as "Big Data" tools.
Some of the features offered by AWS Glue are:
- Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.
- Integrated - AWS Glue is integrated across a wide range of AWS services.
- Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.
On the other hand, Mara provides the following key features:
- Data integration pipelines as code: pipelines, tasks and commands are created using declarative Python code.
- PostgreSQL as a data processing engine.
- Extensive web ui. The web browser as the main tool for inspecting, running and debugging pipelines.
Mara is an open source tool with 1.24K GitHub stars and 51 GitHub forks. Here's a link to Mara's open source repository on GitHub.