ADF is like a Thor in data management universe. This super powerful tool supports ETL operations, ELT Operations and can be used as data orchestration tool. Whereas AWS Glue is primarily ETL/ELT tool that also focuses on governance (data catalog/quality)
Even though both are cloud-based data integration tools, both are different in many aspects. We got a chance to work on both the tools, so we would like to cover/compare both. AWS Glue is ETL/ELT only tool that natively supports data catalog which completely misses in ADF. (But support available through Azure Purview).
- ADF comes with Copy/Move activity with support of 90+ connectors. But ADF is not data migration tool. There are other services in Azure which migrates data to Azure more efficiently.
- ADF supports small to medium transformations with No Code intuitive drag and drop UI. All these transformations will be internally converts to Spark code which can scale seamlessly.
- ADF can handle both structured and unstructured in batch manner or in real time. It implements complex workflows through Azure Data Lake (Azure blob Gen2), HDInsight and Databricks.
- Pipeline creation and monitoring them are efficient with well-built simple UI.
- Glue can connect to 70+ data sources and creates central data catalog with Glue Crawlers. ADF natively don't support catalog but can be implemented using Azure Purview
- Glue also comes with inbuilt UI based transformations but not flexible as compare with ADF. In addition, it will give you options to use Python Shell and Spark. Hence Glue is developer friendly whereas ADF can also be used by domain experts.
- Glue also supports streaming ETL by leveraging AWS Kinesis
- Glue is not orchestration tool by default and should use AWS Steps functions to create pipelines and monitor them.
- Both tools scales seamlessly with serverless architecture.
- Monitoring and creating alerts can be done without installing any external services.
- Event based triggers really helpful in handling real time scenarios. S3 buckets/Azure Blob storage gives flexible storage options in managing data.
- AWS Lambda/Azure Functions are more powerful tools that can handle multiple small jobs (32 jobs maximum with 1.5GB RAM in Azure & 3 GB in AWS)
- Not great if you're not using Cloud - This orchestration tool works great if you're using Cloud.
- Costs - Running anything on a large scale in the cloud can result in a lot of costs really fast and charges separately for different types of activities.
- Limited data integrations - We find integrations and plugins a bit limited and biased towards Microsoft/AWS technologies.
AWS Cloud Engineer | DevOps Engineer | Site Reliability Engineer
8 个月The seamless scalability, integrated monitoring, and event-based triggers make this serverless data analytics solution highly intriguing for real-time scenarios.