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I am working on a problem where we intend to perform multiple transformations on data using EMR (SparkSQL).

After going through the documentation of AWS Data Pipelines and AWS Step Functions, I am slightly confused as to what is the use-case each tries to solve. I looked around but did not find a authoritative comparison between both. There are multiple resources that show how I can use them both to schedule and trigger Spark jobs on an EMR cluster.

  1. Which one should I use for scheduling and orchestrating my processing EMR jobs?

  2. More generally, in what situation would one be a better choice over the other as far as ETL/data processing is concerned?

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Yes, there are many ways to achieve the same thing, and the difference is in the details and in your use case. I am going to even offer yet one more alternative :)

If you are doing a sequence of transformations and all of them are on an EMR cluster, maybe all you need is either to create the cluster with steps, or submit an API job with several steps. Steps will execute in order on your cluster.

If you have different sources of data, or you want to handle more complex scenarios, then both AWS Data Pipeline and AWS Step Functions would work. AWS Step Functions is a generic way of implementing workflows, while Data Pipelines is a specialized workflow for working with Data.

That means that Data Pipeline will be better integrated when it comes to deal with data sources and outputs, and to work directly with tools like S3, EMR, DynamoDB, Redshift, or RDS. So for a pure data pipeline problem, chances are AWS Data Pipeline is a better candidate.

Having said so, AWS Data Pipeline is not very flexible. If the data source you need is not supported, or if you want to execute some activity which is not integrated, then you need to hack your way around with shell scripts.

On the other hand, AWS Step Functions are not specialized and have good integration with some AWS Services and with AWS Lambda, meaning you can easily integrate with anything via serverless apis.

So it really depends on what you need to achieve and the type of workload you have.

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Thanks. Since we need to perform validations, etc. and handle dependencies between jobs, I think EMR steps may not be a clean solution. Key takeaways: 1) For pure data pipeline problems, ADP is better 2) For situations where we want to do complex arbitrary processing, ASF is better.
In case someone come here looking for more answers, this link offers a side comparison of AWS Step Functions with Data Pipeline but still helps to make a decision.
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From Migrating workloads from AWS Data Pipeline - AWS Data Pipeline:

AWS launched the AWS Data Pipeline service in 2012. At that time, customers were looking for a service to help them reliably move data between different data sources using a variety of compute options. Now, there are other services that offer customers a better experience. For example, you can use AWS Glue to to run and orchestrate Apache Spark applications, AWS Step Functions to help orchestrate AWS service components, or Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to help manage workflow orchestration for Apache Airflow.

In my opinion AWS DataPipeline is just an outdated service. Step Functions and Glue workflows are latest options with multiple options to handle the pipelines better.

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If You want to choose between AWS Pipelines and AWS Step Functions You have to determine the focus of the use case , for example -

If your primary concern is ETL and data processing with a focus on EMR jobs, AWS Data Pipelines might be a more specialized and straightforward choice.

If you have complex workflows with conditional logic, error handling, and need a more visual representation of your process, AWS Step Functions might be a better fit.

Other aspect to consider will be ease of use -

AWS Data Pipelines provides a higher-level abstraction for defining data workflows, making it simpler for ETL scenarios. AWS Step Functions, while more versatile, might have a steeper learning curve.

In summary, if your primary focus is on ETL and data processing with EMR jobs, AWS Data Pipelines might be a more specialized and streamlined choice. If you have broader orchestration needs and need a visual representation of complex workflows, AWS Step Functions could be the preferred option.

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