Mastering the Art of Debugging in Data Engineering

As data engineers, encountering bugs and issues in our projects is par for the course. However, instead of viewing them as obstacles, we can see them as valuable learning opportunities to deepen our understanding of our code. In this blog post, we’ll explore the art of debugging in data engineering and share strategies to help you hone your skills and navigate the complexities of debugging.

Understanding the Problem

Before diving into debugging, it’s essential to take the time to thoroughly understand the problem at hand. Rather than jumping to conclusions, carefully read and reread the error message. What is the expected behavior, and what are the symptoms of the issue? Having a clear understanding of the problem will guide your debugging efforts.

Example: Just recently, while running a Spark job, I encountered a situation where the job was terminated, and the Python kernel restarted. I immediately suspected that the issue was related to the driver node, as it hosts the Python kernel.

Google the Error Message: When facing unfamiliar error messages, it’s crucial to conduct a thorough search. Researching the error can provide valuable insights and a better understanding of the issue by seeing how others have addressed similar problems. However, it’s important to remember that every situation is unique. While online resources can offer guidance, it’s essential to delve deeper to understand how their experiences may or may not apply to your specific scenario.

Isolating the Issue

As data engineers, we work with a multitude of tools, often employing several simultaneously to deliver valuable business insights. When encountering errors, it’s common for the root cause not to originate from the current step in the failing pipeline, but rather from a separate step either upstream or downstream. Therefore, it’s crucial to break down the problem into smaller components and isolate the specific area or process where the issue is occurring. By narrowing your focus, you can more easily identify the root cause of the problem and avoid becoming overwhelmed by the complexity of the codebase.

For example, while using an ELT/CDC tool like Qlik to extract data from a SAP Hana source, I encountered an error stating “connection forcibly broken.” Upon investigating, I discovered that the error stemmed from the source database, not from Qlik. This led me to confirm that running the load from another system would result in the same issue observed in Qlik

Reviewing the Code

Go through your code line by line, paying close attention to potential errors or inconsistencies. Look for typos, syntax errors, or logical mistakes that could be causing the issue. Sometimes, the solution to a problem lies in the code itself, and a careful review can reveal the source of the issue.

Using Logging and Monitoring

Incorporate logging and monitoring into your data pipelines to track the flow of data and identify anomalies or errors. Logging can provide valuable insights into what’s happening behind the scenes and help pinpoint the source of the problem. By monitoring your pipelines, you can catch issues early and prevent them from snowballing into larger problems.

Testing and Iterating

Once you’ve identified a potential solution, test it rigorously to ensure it resolves the issue. Don’t hesitate to iterate and refine your approach until the problem is fully resolved. Testing is a crucial step in the debugging process and can help validate your fixes before deploying them to production.

At times, error messages can be unhelpful and misleading, leading you in the wrong direction. For example, we encountered an issue with a CDC tool that reported source latency issues. However, upon investigation, we discovered that the problem actually stemmed from the archival strategy of the source database.

Documenting Your Findings

Maintain detailed records of the debugging process, documenting the steps taken, the solutions attempted, and the outcomes. This documentation will not only track progress but also serve as a valuable reference for future debugging endeavors. Furthermore, sharing your findings with teammates can contribute to building a collective knowledge base and streamline the debugging process for all involved.

This step is crucial and one that I need to adopt more diligently; it will prove beneficial in the long run. I recall working with a company that held weekly review sessions to discuss all production issues encountered and their respective resolutions. The outcome of these meetings was an improvement in development standards.

Conclusion

Mastering the art of debugging in data engineering is a valuable skill that requires patience, persistence, and a methodical approach. By following these strategies and embracing each debugging challenge as an opportunity to learn and grow, you’ll become a more effective and efficient data engineer. Happy debugging!

About Me

I am Nnaemezue Obi-eyisi, a Senior Azure Databricks Data Engineer at Capgemini and the founder of AfroInfoTech, an online coaching platform for Azure data engineers specializing in Databricks.

Follow me on: LinkedIn | All Platforms

To Learn Azure Data Engineering with Databricks, and join the waitlist: Click here