Why Data Pipelines Fail in Production — And How I Build Systems That Last

By Sean Scully | Staff Data Engineer | 13+ Years of Experience

Building a data pipeline that works is one thing. Building one that runs reliably in production every day is a completely different challenge.

Over my 13+ years in data engineering, I've worked with organizations including Thomson Reuters, Apple, Electronic Arts, and John Deere.

I've designed and worked on data platforms using AWS, Azure, Databricks, Snowflake, Apache Spark, Python, and SQL.

One lesson has stayed with me throughout my career:

The best data pipeline isn't just fast. It's reliable, easy to monitor, cost-effective, and simple to maintain.

Here are five common challenges I focus on when designing production data systems.

1. Poor Data Quality Can Break Everything

A pipeline might run successfully while still producing incorrect data.

Missing records, duplicate events, unexpected schema changes, and inconsistent values can cause serious problems for downstream applications.

My approach:

I build data validation into the pipeline instead of treating it as an afterthought.

This includes:

• Schema validation
• Duplicate detection
• Source-to-target reconciliation
• Data completeness checks
• Automated alerts for unexpected changes

These checks make it easier to catch problems early and prevent incorrect data from reaching business reports or AI applications.

2. Pipelines Need to Recover from Failures

In production, failures happen.

APIs become unavailable. Network requests time out. Cloud services experience interruptions. Data arrives late.

A good pipeline needs to handle these situations without requiring constant manual intervention.

My approach:

I design workflows with retry mechanisms, clear error handling, checkpointing, and idempotent processing.

For example, in my work with AWS-based data platforms, I've used services such as AWS Lambda, Step Functions, SQS, and EventBridge to build event-driven processing workflows.

The goal is to make failures easier to detect, recover from, and troubleshoot.

3. Poor Spark Performance Can Increase Cloud Costs

Apache Spark is powerful, but inefficient processing can waste compute resources.

Some common causes include:

• Unnecessary data shuffling
• Poor partitioning
• Data skew
• Expensive joins
• Too many small files
• Reprocessing unchanged data

My approach:

When working with Spark and Databricks, I focus on understanding the execution plan and identifying where processing time is being spent.

I use techniques such as partition tuning, broadcast joins, incremental processing, and Delta Lake optimization.

The goal isn't simply to add more computing power. It's to make better use of the resources already available.

4. Monitoring Should Be Part of the Design

A pipeline that fails silently can be more dangerous than one that stops completely.

Without proper monitoring, teams may discover problems only after business users report missing or incorrect data.

My approach:

I build monitoring and alerting into production workflows.

Depending on the platform, this may include AWS CloudWatch, Azure Monitor, pipeline execution logs, and automated data quality checks.

I want engineering teams to answer three questions quickly:

1. What failed?
2. Why did it fail?
3. How can we recover safely?

Clear visibility reduces troubleshooting time and makes production systems easier to support.

5. Cloud Cost Optimization Starts with Architecture

Cloud platforms make it easy to scale data processing, but costs can increase quickly when pipelines aren't designed carefully.

I've worked on optimizing Snowflake workloads, Spark processing, and AWS data storage.

Some of the techniques I focus on include:

• Incremental ingestion instead of unnecessary full reloads
• Snowflake query tuning and warehouse right-sizing
• Efficient partitioning and file formats
• S3 lifecycle policies and storage tiering
• Removing unnecessary compute and processing steps

A well-designed platform should balance performance, reliability, and cost.

What I Believe Makes a Strong Data Engineer

After working across different industries and technologies, I've learned that successful data engineering requires more than knowing tools.

It requires understanding business needs, making practical architecture decisions, and taking ownership of what happens after deployment.

I focus on building systems that are:

Reliable: They handle failures and recover safely.

Scalable: They can grow as data volume increases.

Cost-effective: They avoid unnecessary processing and infrastructure expenses.

Maintainable: Other engineers can understand and support them.

Business-focused: They deliver data people can trust and use.

Final Thoughts

Every company has different data challenges, but the engineering principles remain similar.

Start with the business