Data Engineering

Data Pipeline Best Practices: From Raw Data to Clean Output

A data pipeline moves data from one or more sources through a series of transformations to a destination where it can be analysed or served. Getting this right from the start saves enormous debugging effort later.

The ETL Pattern

Most pipelines follow the Extract → Transform → Load (ETL) pattern:

1. Extract — pull data from source systems (databases, APIs, files)
2. Transform — clean, validate, reshape, and enrich the data
3. Load — write the result to a destination (data warehouse, dashboard, file)

A variation, ELT, loads raw data first and transforms it inside the destination (common with cloud data warehouses like BigQuery).