AWS Glue vs Azure Data Factory: What are the differences?
AWS Glue: 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; Azure Data Factory: Create, Schedule, & Manage Data Pipelines. It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
AWS Glue and Azure Data Factory belong to "Big Data Tools" category of the tech stack.
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, Azure Data Factory provides the following key features:
- Real-Time Integration
- Parallel Processing
- Data Chunker
Azure Data Factory is an open source tool with 150 GitHub stars and 255 GitHub forks. Here's a link to Azure Data Factory's open source repository on GitHub.
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