Data Pipeline
A data pipeline is the chain of steps that moves data from a source through transformations into a destination — extract, load, transform, deliver.
What is a data pipeline?
A data pipeline is the end-to-end flow that takes data from one or more sources, optionally transforms it, and lands it in one or more destinations. Modern pipelines are scheduled or event-driven, fault-tolerant, observable, and idempotent — re-running them produces the same result.
Why it matters
- Pipelines are how data actually gets used; without them, data sits in silos
- The reliability of every dashboard, every report, every triggered automation depends on the pipeline behind it
- Failed pipelines = silent gaps in reporting, which is worse than visibly broken ones
ETL vs. ELT vs. reverse ETL
- ETL: transform before loading — older pattern, useful when destination is constrained
- ELT: load raw, transform inside the warehouse — modern default for analytics
- Reverse ETL: push warehouse-modeled data back into operational tools (CRM, ad platforms)
How TexAu helps
A TexAu workflow is a small data pipeline — pull, enrich, score, transform, push — with retries, rate limiting, and per-step logging built in.
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