ETL for Reporting

What it is

ETL for reporting shapes raw or lightly-modeled warehouse data into the specific, denormalized structures a BI tool needs — a narrower, more reporting-focused version of the broader data engineering ETL/ELT discipline.

Key points

• Denormalization for query performance: BI tools generally perform best against wide, denormalized tables rather than requiring many joins at query time — reporting ETL often flattens a normalized warehouse schema for this reason.
• Incremental refresh: for large datasets, reprocessing only new/changed data (rather than the full table on every refresh) keeps dashboard refresh times practical as data volume grows.
• Pre-aggregation: computing common aggregations ahead of time (e.g. daily rollups) trades some flexibility for dramatically faster dashboard load times on large datasets.
• Reporting ETL sits downstream of core data engineering pipelines — it assumes clean, warehoused data already exists and focuses specifically on shaping it for consumption, not on the raw ingestion itself.