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7 min read

Data Engineering Anonymous: Why Does Debugging Feel Like a Full-Time Job (and How Can I Fix It)

You know the drill: late nights, long hours, and too much time trying to fix ETL pipelines. Instead of focusing on building powerful and creative solutions, you’re stuck spending hours troubleshooting mysterious errors. Sound familiar?

Debugging is part of the job — it’s how we keep things running. But when debugging consumes more than half your workday? That’s a problem.

We hear it all the time from clients: “My data team spends more time fixing glitches than writing code.” If this resonates with you, you’re not alone. Endlessly fixing messy pipelines, solving cryptic error logs, or untangling malformed data has become an increasingly bigger issue for data engineers. But it doesn’t have to be this way. So, if debugging is taking over your day let’s look at some of the reasons why and explore ways to change that.

Reason #1: The Curse of the Complex Modern Data Stack

Modern data engineering is complex. You’ve got ETL pipelines, cloud data warehouses, streaming platforms, and tools like Airflow all working together — until one doesn’t. And when something breaks, the wider impact can be overwhelming.

Problem: I remember a Kafka-to-Snowflake pipeline breaking down because of a schema change. It seemed simple on the surface, but tracing the issue back to a specific Kafka producer? That required hours of combing through logs and dashboards. It was frustrating, time-consuming, and felt like an endless loop.

Solution: This is exactly why we created Seemore Data. It gives you a clear view of your entire data stack, helps pinpoint issues quickly, and suggests fixes. It turns the nightmare of solving issues into a manageable process, so you can focus on what really matters — building data systems that work.

Reason #2: Rushed Work Can Mean Dirty Data

Data engineers deal with messy, inconsistent data far too often. Rushed implementations lead to incomplete records, mismatched formats, and unexpected errors.

Problem: One time, I ran into an issue with a JSON payload missing required fields. It caused ingestion errors in Snowflake, bringing the whole process to a halt. Everything had to stop while my team hunted down the problem and cleaned up the data.

Solution: Seemore Data can simplify troubleshooting by pinpointing the exact transformation or source causing the issue, saving time and keeping your pipeline running smoothly. It helps you spot data quality issues early, so you don’t end up trying to play catch up when things have gone south. We built Seemore to give clear insights and actionable recommendations, so you can keep your pipelines running smoothly — even when the pressure is on.

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Reason #3: Performance Bottlenecks Slow You Down

Even when everything seems to be working fine, inefficient queries or transformations can bring workflows to a stop. Diagnosing why a query is running slow can often feel like trial and error. So, instead of improving processes, we can end up spending hours tuning performance.

Problem: I have seen it so often — a poorly clustered Snowflake table causes queries to scan unnecessary partitions, making even simple operations painfully slow. The real problem is that it’s rarely just one table. Many data teams we work with suffer from performance bottlenecks and can have a dramatic impact on productivity.

Solution: You can start by optimizing table design using clustering keys to organize data based on frequently filtered columns. This will enable Snowflake to prune unnecessary partitions and speed up queries. Then regularly monitor query profiles to identify inefficiencies, such as excessive partition scans, and adjust clustering as needed. Optimizing these processes using Seemore Data can save time by surfacing poorly clustered tables and offering actionable recommendations, ensuring faster queries and more efficient workflows.

Conquering the Debugging Dragon

Data lineage tools promise a quick fix when it comes to spotting bugs. The problem, however, is that most of these tools are limited in scope, lack granularity and focus solely on metadata-level lineage, missing critical runtime details like transformations or intermediate states of data as it flows through pipelines.
I am proud to say that Seemore Data’s Deep Lineage is a gamechanger and can set data engineers free from the daily debugging grind. This is because its real-time view of all data assets simplifies your data architecture, allowing:

  • Instant surfacing of critical issues and opportunities (cost, unused assets, performance inefficiencies).
  • Root cause analysis: Uncover the root cause of faults and broken dashboards in minutes, not hours, using multi-layered Deep Lineage and AI-powered data summaries.
  • Continuous optimization and low-effort maintenance with bite-sized actions to save costs and boost workflow productivity.
  • Decluttered workflow visualization: Clustering all data assets into clearly visualized workflows reduces “noise” and pinpoints exactly where attention is needed and why.

 

Debugging will always be an essential part of data engineering, but it shouldn’t dominate the day-to-day responsibilities of highly skilled professionals. By streamlining root cause analysis, optimizing workflows, and providing clear visibility into data systems, organizations can empower their engineers to reclaim their time and foster a culture of efficiency and growth. Debugging may never go away entirely, but with the right tools, it can stop feeling like it’s constantly chipping away at the job satisfaction of data engineers.

Are you interested in continuing this discussion directly with Guy? You can message him at guy@seemoredata.io to delve deeper into how to help radically reduce the amount of time your data engineers spend fixing bugs.

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