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AWS Glue vs. Google Dataflow

October 15, 2021

AWS Glue vs. Google Dataflow

As businesses generate more data, they need more advanced ETL (Extract, Transform, Load) tools to process and analyze it. Two popular cloud-based ETL services are AWS Glue and Google Dataflow. In this blog post, we'll compare the features, pricing, and other considerations of the two tools. So, let's dive into the comparison!

Features

AWS Glue provides a managed ETL service that can crawl, transform, and load data from various sources using Apache Spark ETL jobs. It offers multiple job types such as Python Shell, PySpark and Scala to modify and transform data from a variety of sources.

Google Dataflow is a fully-managed streaming analytics service that allows businesses to build batch pipelines and streaming ETL pipelines using Apache Beam. With Google Dataflow, businesses can create complex data pipelines with a unified programming model across streams and batches.

Pricing

AWS Glue offers pay-as-you-go pricing, where you pay only for the resources that you consume. The pricing model includes an hourly rate for the AWS Glue ETL job and an hourly rate for each additional worker node used to scale the job. In addition, there are data processing and data catalog fees.

Google Dataflow’s pricing is based on usage and offers a free tier to get started. Beyond that, the pricing model is based on the number of vCPU seconds and memory used during processing.

Considerations

The choice between AWS Glue and Google Dataflow ultimately depends on your business needs. AWS Glue is a good choice if you are looking for a fully managed and powerful ETL solution with numerous job types and integration with other AWS services. On the other hand, Google Dataflow is better suited for businesses that require real-time data processing with a unified programming model across batches and streams.

References

Below are the references used in this blog post.

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