6 Spark Data Engineering Pitfalls to Avoid

1. Serialisation Bottlenecks

Poor serialisation strategies often lead to job slowdowns, increased network traffic, and out-of-memory errors.

Avoid it by:

• Using efficient serialisers like Arrow, not Pickle

• Avoiding complex nested data types

• Filtering and aggregating data before network transfer

2. Out of Memory Errors

Common in large jobs with inefficient memory allocation, poor GC tuning, or high shuffle overhead.

Avoid it by:

• Right-sizing driver/executor memory settings

• Leveraging dynamic allocation

• Using off-heap memory where appropriate

• Monitoring with Spark UI or external tools like Prometheus

3. Long Running Jobs

Slow jobs drain resources and delay delivery of business critical data.

Avoid it by:

• Rewriting inefficient transformations

• Reducing data shuffling and using broadcast joins

• Scaling resources based on job type

• Reviewing stage durations and DAG complexity in Spark UI

4. Data Skew

Uneven distribution across partitions leads to stragglers and unnecessary bottlenecks.

Avoid it by:

• Analysing join keys for skew

• Using salting techniques or pre-aggregation

• Repartitioning based on actual data distribution

5. The Small File Problem

Thousands of small files result in inefficient parallelism and resource exhaustion.

Avoid it by:

• Compacting with .repartition() or .coalesce()

• Writing with optimal block sizes

• Using sequence files or Hadoop Archive (HAR) formats

6. Over-Reliance on Hand-Coded Pipelines

Custom PySpark scripts can be fragile, hard to maintain, and slow to scale.

Avoid it by:

• Adopting low-code tools like Prophecy for reusable pipeline design

• Standardising data transformations

• Focusing engineers on business logic, not boilerplate code

Why It Matters

In the real world, slow is bad, errors are bad because they cost time and money. It doesn’t need to happen, Databricks and Spark are incredible tools if you know how to optomise them. With the right advice you can have real-time reporting and AI-driven insights for less money than you spend on a poor experience.

Spark pipeline performance, directly impacts business performance

Poorly optimised ETL jobs don’t just increase cloud spend,  they delay decisions, limit agility and erode trust in your data.

At Cloudaeon, we help enterprises modernise their Spark estate from code-level tuning to platform wide automation to deliver trusted, efficient and scalable data pipelines.

Want to assess your Spark pipelines?

Ask your account manager about our 72 hour ETL benchmark  service, it's fast, focused and fully aligned to business value.