Why Your Data Pipeline Is Slower Than It Should Be And How to Fix It

Imagine this: your data pipeline is scheduled to run overnight, and by morning, you expect fresh, updated data ready to use. But instead, it’s still running, dashboards are outdated, and your team is waiting. You begin troubleshooting only to find that performance issues are creeping in again.

If you’re working in data engineering, you’ve likely encountered this scenario. Fortunately, most slowdowns can be avoided with a few targeted improvements. In this post, we’ll explore some of the most common reasons pipelines slow down and practical strategies to resolve them.

1. The Small Files Problem

One of the most common and overlooked performance issues is the creation of too many small files. Distributed engines like Spark, Hive, or AWS Athena struggle when they need to read thousands of tiny files. Each file opening and closing introduces overhead, significantly slowing down read performance.

How to resolve:

• Use file compaction strategies to merge smaller files into larger ones.

• Aim for output files in the range of 100–500 MB when possible.

• Avoid writing output files per individual record; instead, batch writes during processing.

Note: If your data lake contains hundreds of thousands of files per partition, it’s time to revisit your file-writing logic.

2. Inefficient Filtering of Data

Pipelines often read more data than necessary and then apply filters after loading. This wastes compute resources and increases latency.

How to resolve:

• Apply filters as early as possible, ideally in the source query (e.g., SQL WHERE clauses).

• Partition your data effectively by common query fields such as date, region, or source.

• Read only relevant partitions or prefixes (e.g., from S3) instead of scanning full datasets.

Example: Instead of reading an entire month’s worth of logs, filter for a specific date during the read step.

3. Join Performance Issues

Joins are powerful but can be expensive if not executed properly. Large table joins, skewed data, or lack of optimization can lead to major slowdowns in your pipeline.

How to resolve:

• Use broadcast joins when one of the tables is small enough to fit in memory.

• Repartition data by the join key to balance shuffle across nodes.

• Inspect execution plans or Spark UI to identify any bottlenecks during join stages.

Tip: Be mindful of data skew a single key with disproportionate data can cause stage failures.

4. Suboptimal Spark Configuration

Many data pipelines rely on Apache Spark, but out-of-the-box configurations are rarely optimized for large workloads. Misconfigured resources can silently degrade performance.

How to resolve:

• Tune spark.sql.shuffle.partitions based on the size of the data.

• Use coalesce() to reduce the number of output files after transformations.

• Monitor and adjust executor memory, cores, and instances to match your workload size.

As a general guideline, set shuffle.partitions to approximately the total data size divided by 128 MB.

5. Schema Evolution and Inconsistencies

Changes in schema such as new columns or data type shifts can cause serialization issues, unexpected nulls, or unnecessary data reprocessing.

How to resolve:

• Use data formats that support schema evolution, such as Apache Parquet, Avro, or frameworks like Apache Hudi.

• Maintain schema versioning or a schema registry to validate consistency.

• Add schema checks during ingestion and transformation stages to prevent silent errors.

Logging the inferred schema at each stage can help you identify unexpected changes early.

6. Throttling from External API or Services

If your pipeline interacts with external services such as enriching data via an API or writing to DynamoDB excessive concurrency can lead to throttling and failures, which delay your job.

How to resolve:

• Implement rate-limited batching when calling external APIs.

• Use AWS services like SQS and Lambda to control concurrency.

• Add exponential backoff and retry mechanisms with limits.

If your pipeline is sending thousands of concurrent requests to a single endpoint, this is likely a problem.

7. Data Clutter and Unnecessary Columns

Keeping too much data throughout the pipeline such as unused fields, logs, or intermediate artifacts can bloat storage and slow down processing.

How to resolve:

• Drop columns that are not required in downstream processes early in the pipeline.

• Delete temporary or intermediate files that are not part of long-term storage.

• Archive or move infrequently accessed data to lower-cost storage tiers like S3 Glacier.

A leaner pipeline is a faster and more cost-effective one.

Final Thoughts: Monitor and Optimize Regularly

Building efficient pipelines is an iterative process. It’s important to continuously monitor job performance, resource utilization, and data volume. Tools such as Spark UI, AWS CloudWatch, or Datadog can help you gain insights into where slowdowns are happening.

Many of these optimizations are simple to implement and can dramatically improve performance and reduce costs. Investing time in pipeline tuning leads to better reliability, faster insights, and a more scalable architecture.