You might be working on a new analytics platform, trying to figure out how users behave, or just learning more about data pipelines. That is fantastic! Decision-making and insights are made possible in large part by data engineering. However, data engineering has its own distinct set of hidden pitfalls that can turn a promising project into an overnight debugging session, just like any other specialized field.
Knowing these typical errors can save you (and your team) a great deal of suffering, regardless of whether you are a full-time data engineer, a software engineer experimenting with data, or a product manager attempting to comprehend the technical landscape. Let us examine ten common pitfalls that I have observed.
1. The "Current Date" Deception
Using current_date or now() in your data processing seems okay, right? It just grabs the current timestamp. What could go wrong?
- The Pitfall: When you use
current_datetimestamp indicates when the data was processed, not when the event occurred. This becomes a significant issue when dealing with late-arriving data or needing to backfill historical information. - Example: A daily sales aggregation job runs on October 27th to process sales data for October 26th. If it stamps records with
current_date, those sales will incorrectly appear to have occurred on the 27th. - Smarter Move: Keep using an event timestamp; that’s an inherent part of the source data. If it’s a sale, use the
sale_timestamp; if it’s a user click, use theclick_timestamp. When processing data, refer to the event timestamp. For example, if you are using Airflow, checkout how to use the logic/execution timestamp from Airflow Macros
2. The Sneaky Skew: When Data Plays Favorites in Spark
Imagine you’re dividing up tasks, but one person gets 90% of the work. This phenomenon, known as data skew, significantly reduces performance in distributed systems such as Apache Spark.
- The Pitfall: A few tasks (running on particular executors) in Spark become overloaded with data if your data is not split up evenly across partitions. These turn into bottlenecks, causing some tasks to take hours to complete while others only take minutes. This procedure slows down the Spark job as a whole, which could result in missed SLAs or stage failures.
- Example: You’re running a Spark job to aggregate user activity data, partitioned by
user_id. The Spark task in charge of that power user’s partition will be a straggler, causing the entire task to be delayed, if one “power user” (such as a well-known social media account) has millions of events while the majority only have a few. - Better Move: Watch for imbalances in task duration in your Spark UI. Methods such as employing AQE (Adaptive Query Execution) features for skew handling or “salting” your join or group-by keys (adding a random element to distribute data more evenly across Spark partitions) can be invaluable.
3. "It'll Be Clean, I Swear!" - The Data Quality Gamble
We have all been tempted to make the rookie error of assuming that source data will arrive flawless.
- The Pitfall: Your pipelines may unintentionally contain unexpected nulls, incorrect data types, unexpected duplicate records, or simply nonsensical values. This ruins your analytics, contaminates downstream datasets, and is difficult to fix after the fact.
- Example: An
orderstable suddenly starts receiving theorder_totalas a string with currency symbols (e.g., “$123.45”) instead of a clean numeric value. All your downstream revenue calculations, possibly running in an SQL-based transformation tool or a Spark job, break. - Better Move: A wiser move would be to apply quality checks and data validation frequently, starting at the point of ingestion and important transformation. This can be automated by strategically placed SQL assertions in your DBT models or Spark SQL queries.
4. The Rerun Nightmare: Forgetting Idempotency in Airflow Tasks
Idempotent operations are those that can be repeated with the same input and produce the same result each time. Retries are required when using workflow orchestrators such as Apache Airflow, and they can be carried out without human intervention.
- The Pitfall: A non-idempotent task can cause chaos in your target database or data lake, including duplicate data, incorrect aggregations, or a generally disorganized state, if an Airflow task that modifies data fails halfway through and Airflow automatically tries again.
- Example: An Airflow DAG may include a task that inserts new user sign-ups into a table. If the task fails after inserting half of the records for a given run and Airflow retries it without idempotency checks (such as determining whether a user ID already exists before inserting), you will have duplicate user entries.
- Smarter Move: Design your data writes, especially within loading operations, to be idempotent. Use
INSERT OVERWRITEfor full table or partition refreshes, or employMERGE(often called UPSERT) operations that intelligently insert new records or update existing ones based on a key.
5. The Ever-Shifting Sands of Schema Evolution
Particularly when it comes to data schemas, change is the only constant. Types are changed, new columns are added, and occasionally fields disappear completely.
- The Pitfall: When the source changes, pipelines will unavoidably break, especially those in Spark that may infer schemas or are coded to expect a rigid structure.
- Example: Your Spark job is contentedly reading Parquet files in anticipation of a particular 10-column schema. One day, an eleventh column is added by an upstream process. The job may fail because of a schema mismatch, or if schema merging is not set up, it may silently drop the new column, depending on your Spark read options.
- Better Thing to Do: Develop resilience! Make use of your tools’ schema detection and evolution features (Spark offers strong schema merging and evolution support for formats such as Parquet and Avro). Use custom checks or schema registries to keep an eye out for schema drift.
6. The "Too Many Tiny Files" Trap for Spark Performance
An army of tiny files is your worst enemy when it comes to performance in distributed file systems (think HDFS, Amazon S3, and Google Cloud Storage), especially for Apache Spark.
- The Pitfall: The drawback is that each task frequently handles a collection of files, and Spark’s driver must list every file for a dataset. Slow queries and stalled Spark jobs result from listing and opening thousands of tiny files, which adds significant overhead for the driver and is inefficient for the executors.
- Example: A Spark Streaming job writing to S3 using
forEachBatchtaht creates a new Parquet file every few seconds for each micro-batch. By the end of the day, a single table partition might contain tens of thousands of these tiny files, making subsequent Spark SQL queries on that table painfully slow. - Smarter Move: Implement compaction processes. These are often separate Spark jobs or operations within your pipeline (e.g., using
OPTIMIZEin Delta Lake orREPARTITIONbefore writing) that periodically gather small files and merge them into larger, more optimized ones. Adjust batch sizes for streaming outputs.
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- Get a gentle overview of big data and Spark
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- Debug, monitor, and tune Spark clusters and applications
- Learn the power of Structured Streaming, Sparkâ s stream-processing engine
- Learn how you can apply MLlib to a variety of problems, including classification or recommendation
7. Flying Blind: The Peril of No Monitoring or Misconfigured Airflow Alerts
Does a tree make a sound when it falls in the forest and no one is around to hear it? Is your data still reliable if your vital Airflow DAG malfunctions and its alerts are either misconfigured or route to an unmonitored channel?
- The Pitfall: Discovering pipeline failures can take hours or even days. Bad data penetrates your systems, ruining dashboards and reports and resulting in poor business choices. Simply having Airflow doesn’t mean alerts are effective.
- Example: The nightly Airflow DAG, which is crucial for updating your main sales dashboard, has failed. We configure the alerting to send an email, but it ends up reaching an old, unmonitored distribution list. For the next 12 hours, executives will be reviewing outdated data, completely unaware that Airflow is “technically” sending an alert.
- Smarter Move: There is no way to compromise on thorough monitoring. Monitor data volumes, important data quality metrics, and pipeline status (success, failure, duration using Airflow’s user interface and logs). It is crucial to make sure your Airflow alerts—or alerts from any orchestrator or monitoring system—are set up, tested, and routed to a PagerDuty or channel that is actively monitored.
8. The Mystery Tour: Missing Data Lineage
“Where did this number come from?” If you’re unable to answer that, it may indicate a lineage issue. Data lineage is the map of your data’s journey — its origins, transformations (including those complex Spark stages or Airflow task dependencies), and destinations.
- The Pitfall: Without clear lineage, troubleshooting issues, understanding the impact of changes, or even just validating data becomes an archaeological dig. Debugging turns into a time-consuming forensic investigation.
- Example: A key financial report shows an unexpected nosedive in customer acquisition. Without a clear history, figuring out which source system, specific Spark transformation, or Airflow DAG run caused this problem is a huge challenge.
- Smarter Move: Invest in data lineage tools if you can (some integrate with Spark and Airflow). If that’s not possible, please ensure the flow of data is meticulously documented. Know your sources, understand your transformations, and track your destinations. Good commit messages and a well-documented code also help.
9. The Security Shortcut: Hardcoding Secrets
It is convenient, but a bad idea, to embed database passwords, API keys, or even file paths straight into your code (whether it is a Spark application or a Python script for an Airflow operator).
- The Pitfall: It’s a massive security risk if those credentials leak (e.g., code checked in a public repository). It also complicates the process of promoting code between different environments (dev, staging, and production), necessitating code changes each time.
- Example: A Python script used within an Airflow
PythonOperatorcontains a hardcoded database password to connect to a source. If this DAG file is checked into version control, that password becomes visible to anyone with access to the repository. - Smarter Move: Use Airflow Connections & Hooks, which leverage its secret backend (like AWS Secrets Manager or Google Cloud Secret Manager). For Spark, retrieve secrets from these systems at runtime or use secure credential passthrough mechanisms available in your cluster environment.
10. Reinventing the Flat Tire: Ignoring Existing Tools like Airflow and Spark
The data engineering ecosystem is bursting with powerful open-source tools like Apache Airflow for orchestration and Apache Spark for processing, not to mention managed cloud services.
- The Pitfall: Spending precious engineering cycles building custom solutions for things that are already expertly handled by existing tools. This procedure slows you down and adds to your long-term maintenance burden.
- Example: Writing a complex, custom cron-based Python scheduler with intricate retry logic, dependency management, and distributed task execution from scratch, when a tool like Apache Airflow provides all this (and a UI, logging, alerting hooks, etc.) out of the box. Alternatively, one could write complex data transformations in vanilla Python for large datasets, but Apache Spark is specifically designed to handle such tasks at scale.
- Smarter Move: Make use of well-known frameworks such as Spark for distributed processing and Airflow for scheduling. Concentrate your special engineering talent on resolving business issues that are genuinely unique to your field.
Final Thoughts
The field of data engineering is extremely rewarding despite its challenges. By avoiding these typical mistakes, you can more easily handle the complexity, create more robust systems, and eventually extract more value from your data—especially when using strong tools like Airflow and Spark.
Have you encountered any of these pitfalls? Share your hard-earned lessons or favorite tools in the comments — let’s learn together!
