What is a Data Pipeline and what are the steps to build one from scratch?

All answers to this question.

A data pipeline is a series of automated steps that ingest raw data from various sources and move it to a destination for storage and analysis. The stages include Ingestion (gathering data), Transformation (cleaning and formatting), and Loading (moving to a warehouse). To make it resilient, you must implement error handling, logging, and "retries." For instance, if a source API is down, your pipeline should be able to pause and try again later without manual intervention. Using an orchestrator like Airflow helps manage these dependencies and provides alerts when things go wrong.

   Answered 2025-11-22 by Cynthia King

When building these, do you find it more efficient to use "No-Code" tools for ingestion or is custom Python scripting still the preferred method for most engineers?

   Answered 2025-11-24 by Patrick Sullivan

• Patrick, it depends on the scale. No-code tools like Fivetran are great for standard sources (like Salesforce), but custom scripts give you the flexibility needed for proprietary data or complex transformations that those tools can't handle.

     Commented 2025-11-25 by Edward Lawson

Think of it like a plumbing system; you need to ensure there are no leaks (data loss) and that the water (data) is purified before it reaches the faucet.

   Answered 2025-11-26 by Donna Howard

• That's a perfect analogy, Donna! Monitoring and alerting are the sensors that tell you if there's a leak in the system before the user notices.

     Commented 2025-11-27 by Scott Lewis