Data Pipelines & Transforms
When someone says 'we have a data pipeline,' they mean: automated steps that take raw inputs and produce clean, structured outputs. No more copy-paste from Excel into a dashboard.
- Pipeline = extract (get data) → transform (clean, reshape, join) → load (into a usable form)
- Transforms are the rules: standardize units, normalize names, fill gaps, validate ranges
- Why separate transform from load? So you can rerun transforms without re-extracting
- dbt, SQL, Python—different tools, same idea: codified transformation logic
- The goal: raw data in, decision-ready data out. Repeatable. Auditable.
Real-world example
Supplier sends 10 Excel files with different formats
Each file has "recycled %" in a different column. One uses decimals (0.5), another percentages (50). Dates are DD/MM, MM-DD, and "Q1 2024."
- Extract: Pull files from email, Drive, or API. Get raw bytes into the system.
- Transform: Map "recycled %" from whichever column it's in. Normalize 0.5 and 50 to same format. Parse all date variants to ISO. Join with SKU reference table.
- Load: Write clean rows to a table (or datastore) that analytics and dashboards read from.
- Next week: New files arrive. Pipeline runs again. Same transforms. Same output shape. No manual cleanup.
- The pipeline is the contract: "Give us messy input; we give you clean output."
Pipelines turn "someone sent a spreadsheet" into "we have queryable, validated data."
A pipeline is a sequence of steps that moves data from source(s) to destination(s), with transformations in between. Extract (get it), Transform (clean and reshape it), Load (put it where it's needed). Sometimes ELT: load raw first, transform in place.
Raw data is inconsistent. Transforms apply rules: standardize units, normalize names, validate ranges, join with reference tables. The output becomes decision-ready—same structure, same semantics, every run.
Transforms should be codified (SQL, dbt, Python). That way they're repeatable, versioned, and testable. Manual fixes don't scale.
- Multiple sources that need to be combined
- Data that arrives regularly (daily, weekly) and needs the same treatment
- Transform logic complex enough that manual work is error-prone
- You need an audit trail: what ran, when, what changed
- Baking transforms into reports instead of a dedicated layer—hard to reuse and test
- No failure handling—pipeline breaks, bad data flows through
- Transforms that are opaque—no one knows why a value became X
- Treating the pipeline as one-off scripts instead of maintained infrastructure
Need a data pipeline for your analytics?