ETL Modernization with PySpark

ETL modernization is the process of updating and improving traditional ETL workflows and systems to meet the evolving needs and challenges of modern data integration and analytics. PySpark is a popular open-source distributed computing framework that can be used for ETL processing. One of the key benefits of using PySpark for ETL is that it enables programmatic ETL. This means that ETL pipelines can be written in code, which offers a number of advantages over GUI-based ETL tools. For example, programmatic ETL is more flexible, scalable, and it allows for greater automation.

PySpark programmatical ETL versus GUI-based ETL

PySpark programmatic ETL and GUI-based ETL are two different approaches to ETL (Extract, Transform, and Load).PySpark programmatic ETL involves writing ETL code in PySpark, a popular open-source distributed computing framework. This approach offers a number of advantages over GUI-based ETL tools, including:

• Flexibility: Programmatic ETL allows you to create custom ETL pipelines that are tailored to your specific needs. GUI-based ETL tools typically offer a limited set of pre-built components, which can be restrictive.
• Scalability: PySpark is a distributed computing framework, which means that it can scale to handle large datasets. GUI-based ETL tools are typically not as scalable.
• Automation: PySpark code can be easily automated using tools such as Apache Airflow or Prefect. This can free up your team to focus on more strategic tasks.
• Performance: PySpark is optimized for distributed computing, and it can take advantage of multiple cores and processors. This can lead to significant performance improvements over GUI-based ETL tools.

GUI-based ETL tools provide a graphical user interface for building and deploying ETL pipelines. This approach can be easier to get started with than programmatic ETL, but it can be less flexible and scalable.

Challenges of converting existing ETL code to PySpark code (potential targets include Azure Databricks, Synapse Notebooks, Fabric Notebooks, Spark Job Definition, EMR and AWS Glue)

Converting existing ETL code to PySpark code can be a challenging task. There are a number of reasons for this, including:

• Different programming paradigms: PySpark and traditional ETL tools use different programming paradigms. This means that the way code is written and executed differs significantly between PySpark and traditional ETL tools.
• Complexity of PySpark: PySpark is a complex framework with a wide range of features, especially if you are not already familiar with distributed computing paradigm.
• Lack of documentation: There is a lack of documentation on how to convert existing ETL code to PySpark code. This can make the conversion process challenging, especially if you are trying to convert complex ETL logic.
• Availability of skilled (PySpark) resources or learning curve : The challenge lies in finding proficient resources to handle the conversion of current ETL code to PySpark code. Since PySpark is a programming language and platform, there is a learning curve involved. It is imperative to allocate time and resources for training the team on PySpark and the new platform. The existing ETL developers may find it challenging to acquire proficiency in utilizing PySpark efficiently for ETL processes.
• There are various frameworks available in the market, such as Databricks, Synapse Notebooks, Fabric Notebooks, and AWS Glue, that offer built-in capabilities for spark programmatic ETL development. These frameworks optimize the underlying process execution and enable access to native cloud products, such as storage, key vault, and database. However, due to the abundance of available frameworks, it can be challenging to convert to a specific one.

Using automation to overcome challenges (Bitwise approach)

There are several things that you can do to overcome the challenges of converting existing ETL code to PySpark code for Azure Databricks and AWS Glue:

• Comprehensive Assessment: Conducts a comprehensive analysis of the source ETL code base, identifying any uncertainties, intricacies, and data lineage for better planning. (Any source code ETL analyzer tool will be helpful)
• Start small: Don’t try to convert all of your ETL code to PySpark at once. Start by converting a small subset of your code, and then gradually convert the rest of your code over time.
• Use a modular approach: Break down your ETL code into small, modular components. This will make the conversion process easier and more efficient.
• Use a code conversion tool: There are a number of tools that can help you to convert your existing ETL code to PySpark code. These tools can save you a significant amount of time and effort.
• Test your code thoroughly : Once you have converted your ETL code to PySpark, be sure to test it thoroughly to make sure that it is working correctly. (Any automation testing tool will be helpful)
• DevOps Cycle : DevOps can automate ETL pipeline build, testing, and deployment through CI/CD. Monitoring and alerting can detect issues and ensure smooth pipeline performance. Shift-left testing can detect and fix issues early in the development cycle. DevOps can improve collaboration between data engineering and other teams for timely and efficient ETL pipeline development and deployment.
• Deploy the new ETL solution: Once the testing is complete, deploy the new ETL solution in the Dev/QA/Production environment.
• Train the users: Train the users on the new ETL solution and provide them with the necessary documentation and support.
• Monitor and optimize the new ETL solution: Monitor the new ETL solution for any issues and optimize it for better performance (if required).