AWS Glue vs Google Cloud Dataflow: Which one wins the race?
Data processing and ETL (Extract, Transform, Load) are two essential processes in data management. They help assess the quality of data, transform data to suit different requirements, and finally, load the data into a target database for use. Deploying tools like AWS Glue and Google Cloud Dataflow to address ETL and data processing needs can be both efficient and effective. In this post, we provide a comparison of these two cloud deployment tools, AWS Glue and Google Cloud Dataflow, so that you can choose the right tool for your needs.
AWS Glue
AWS Glue is a serverless ETL tool that helps develop, run, and monitor data-processing workflows. It was launched by Amazon Web Services (AWS), and it can automatically generate Python code for ETL jobs. With AWS Glue, you can perform various tasks, such as data discovery, schema discovery, and automatic schema mapping. AWS Glue is incorporated with other AWS services that include Amazon S3, Amazon RDS, Amazon Redshift, and Amazon Aurora.
AWS Glue Pricing
AWS Glue pricing is based on two components:
- The hourly rate based on the amount of processing units used
- The cost of storing metadata generated during ETL jobs, which is stored on AWS Glue catalog.
Google Cloud Dataflow
Google Cloud Dataflow is another cloud deployment tool that offers managed data processing for batch and stream processing jobs. Google Cloud Dataflow offers a simplified model to handle data processing with its pre-built transformation libraries for building pipelines quickly. Google Cloud Dataflow integrates with other Google services such as Google BigQuery, Google Storage, and Google Cloud Pub/Sub.
Google Cloud Dataflow Pricing
Google Cloud Dataflow pricing depends on the number of worker-hours used to process the data, the amount of data stored on the platform, the number of incoming jobs, and the number of data sources.
AWS Glue vs Google Cloud Dataflow Comparison
| Criteria | AWS Glue | Google Cloud Dataflow |
|---|---|---|
| Ease of Use | AWS Glue is very easy to use for users familiar with AWS | Google Cloud Dataflow provides a simplified model to handle data processing |
| Programming language | AWS Glue uses only Python | Google Cloud Dataflow uses multiple languages such as Python, Java, and Kotlin |
| Scalability | AWS Glue can handle various data processing requests simultaneously | Google Cloud Dataflow scales easily and automatically, depending on demand. |
| Integration | AWS Glue integrates with other AWS services | Google Cloud Dataflow includes integration with Google Storage, Google Pub/Sub, and BigQuery |
| Pricing | AWS Glue pricing is based on the hourly rate and the cost of storing metadata | Google Cloud Dataflow pricing depends on the number of worker hours and data stored. |
Final Thoughts
Both AWS Glue and Google Cloud Dataflow are excellent cloud deployment tools that offer efficient data processing for ETL processes or batch data processing. AWS Glue has a very low entry barrier if you already have an AWS account, and it integrates well with other AWS services. On the other hand, Google Cloud Dataflow has a simplified model to handle data processing and supports multiple languages. When it comes to pricing, both tools offer flexibility and transparency, and we recommend that you compare the pricing based on your specific needs.
That's it for the comparison between AWS Glue and Google Cloud Dataflow! We hope you found this post helpful in choosing the right tool for your ETL or batch data processing needs!