Expert Advice: Build or Buy for data export?

Kevin Suer, Director of Product for Zuora’s Data Platform, built Zuora’s data export features, which support 14 destinations and replicate over 1 billion rows of data per month. We asked what advice he would give to anyone evaluating whether to build or buy a data export solution. Here’s what he said:

1. Understand the Hidden Complexity of Building

At first glance, building an in-house data export pipeline seems simple—write a script, hit an API, and send data to a warehouse. But in reality, it’s far more complicated. Challenges include:

• Data model complexity – Enterprise applications often have nuanced object models that introduce unexpected complexity.
• Data integrity and consistency – Ensuring accuracy and completeness is critical, especially for financial systems.
• Ongoing schema changes – Data models evolve over time, requiring constant updates to maintain stability.
• Monitoring and on-call support – When a pipeline breaks, especially during high-stakes moments like financial reporting, you need dedicated resources to fix it fast.
• Scaling across multiple data destinations – Supporting one warehouse (e.g., Snowflake) may seem manageable, but expanding to others (e.g., BigQuery, Redshift) adds significant development and maintenance overhead.

2. Prioritize Core Business Needs

Even for an experienced data team like ours, building and maintaining a robust export pipeline wasn’t the best use of engineering resources. I recommend asking yourself:

• Is data pipeline management our core competency?
• Would we rather focus on business-critical innovation? 

By outsourcing this problem, we freed up our engineering team to work on more strategic initiatives around analytics and monetization.

3. Avoid Partial Solutions That Frustrate Customers

One common mistake I see is companies launching a data export feature with support for only one destination, thinking they’ll add more later. This often backfires:

• Customers who use other platforms feel left out, creating friction.
• Sales teams lose deals because they can’t meet customer requirements fast enough.
• The company risks appearing incomplete or unprepared to serve enterprise needs.

Instead, I suggest planning for broad destination coverage upfront—either by investing heavily in internal development or partnering with a provider like Prequel that supports multiple warehouses out of the box.

4. Data Trust Is Hard to Earn and Easy to Lose

Once customers start using a data export feature, they expect it to be 100% reliable. If a pipeline breaks repeatedly, trust erodes quickly, and recovering from that damage is difficult. We valued Prequel’s ability to ensure data accuracy, compliance, and reliability—especially for financial customers who can’t afford inconsistencies.

Final Takeaway

Building a data export pipeline in-house might seem feasible at first, but the long-term costs—both in engineering effort and customer experience—can be far greater than expected. We ultimately chose Prequel because it allowed us to move faster, serve more customers, and maintain focus on our core product. My advice? Think beyond the first warehouse, plan for scale from the start, and don’t underestimate the hidden complexity of data exports.