Do Not Underestimate the Power of UX in Travel

Why data pipelines fail at scale

Most data pipelines work perfectly in development. They process a few thousand records, run on a single machine, and produce results within seconds. The problems begin when you try to scale - when the data volume grows by 10x, when multiple services start writing to the same source, or when you need the pipeline to run in real-time instead of batch.

The root cause is almost always the same: the pipeline was designed for the data you had, not the data you'll have. This section walks through the most common architectural mistakes and how to avoid them from the start.

The three layers every scalable pipeline needs

A production-ready data pipeline isn't a single process - it's a system with three distinct layers, each with its own concerns:

Ingestion layer - responsible for collecting data from source systems. This layer needs to handle backpressure, retries, and schema changes without breaking downstream processes.

Processing layer - where data is transformed, validated, and enriched. The key question here is whether you need batch processing, stream processing, or both. Most AI products need both.

Storage layer - where processed data lands. For AI workloads, this often means both a feature store for model inputs and a data warehouse for analytics.

Choosing between batch and stream processing

This is the decision that causes the most confusion in early-stage AI products. Batch processing is simpler, cheaper, and easier to debug. Stream processing is more complex but enables real-time features.

The mistake most teams make is choosing stream processing because it sounds more advanced, then spending months dealing with complexity that wasn't necessary. Start with batch. Move to streaming only when you have a concrete requirement that batch can't meet and when you have the engineering capacity to maintain it.

Monitoring and observability

A pipeline that runs without monitoring isn't a production pipeline - it's a time bomb. At minimum, you need to know when a pipeline fails, how long it takes, and whether the output data looks correct. That last point is the hardest: data quality monitoring requires defining what "correct" looks like for your specific domain.

Tools like Great Expectations or dbt tests can automate data quality checks, but someone needs to define the rules. This is a product decision as much as a technical one.

Key takeaways

Build for the data you'll have, not the data you have now. Separate ingestion, processing, and storage concerns from day one. Start with batch processing and move to streaming only when you have a concrete requirement. And invest in monitoring before you need it - not after your first production incident.