Data that lives in source systems is not data you can use. ETL pipelines are what move it to where decisions get made.

Scheduled extraction from ERP, CRM, WMS, flat files, databases, and SaaS APIs with orchestration that makes each run reliable, observable, and recoverable. Incremental loads use watermarks so large tables aren't re-extracted each run, SAP extraction avoids direct database access, and retry logic with exponential backoff surfaces genuine failures without false alarms.

Raw data lands in the warehouse in its source form before transformation runs, the ELT pattern that preserves source fidelity and lets transformations be rebuilt without re-extracting. The transformation layer runs a version-controlled dependency graph with standard staging, intermediate, and mart layers. Incremental materialisations process only new or changed records, so warehouse compute on large tables drops sharply instead of re-scanning hundreds of millions of rows each run, and column lineage documents how each dashboard metric was derived.

Connectors for the SaaS tools that hold business data, built with the rate limiting, pagination, and incremental pull patterns that make production ingestion reliable. OAuth tokens rotate automatically from a secrets manager, so pipelines don't fail on expiry, and custom REST connectors for internal systems typically take 3 to 5 days of engineering each.

Change Data Capture replication that captures every insert, update, and delete as it happens, without full table scans or polling that loads the production database. Change events stream from the database transaction log to the target system, and full row history is preserved in the warehouse with SCD2 patterns for compliance and analytics. CDC is the right choice where deletes must propagate, tables lack a reliable updated_at column, or latency must stay under a minute.

Run logging, row count validation, schema change detection, and freshness SLA monitoring across all pipeline jobs, so failures are discovered by the monitoring system, not by an analyst noticing a stale dashboard. A run delivering 10% of the normal record count alerts instead of silently loading partial data, schema changes that would break transformation models alert before the load step runs, and one central dashboard shows run status and alert history across all jobs.

SQL models that define your business entities, metrics, and reporting constructs in version-controlled SQL with full lineage, replacing ad-hoc analyst queries and undocumented spreadsheet formulas. Models follow the staging-intermediate-mart convention, each with one purpose, so changes don't ripple. Tests on every model catch duplicates, nulls, and broken relationships, and source freshness checks fail fast rather than producing stale marts.