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The difference between a data pipeline that works and one that works reliably is in the details that are invisible when everything is running: schema change handling, incremental load logic, deduplication for at-least-once delivery systems, backfill capability when a pipeline needs to reprocess historical data, and the alerting that fires within minutes when a pipeline silently fails.
For each data source: access method (API, database, file), available extraction patterns (full load, incremental, CDC), schema documentation, volume estimates, and any extraction constraints (API rate limits, database load impact).
Latency requirements per source determine the pipeline pattern: batch, streaming, or CDC. Transformation logic mapped to a data flow diagram showing source, transformation steps, and target. dbt model architecture designed before any SQL is written.
Pipelines built in order of business priority. Each pipeline includes extraction, transformation, loading, deduplication where required, and monitoring instrumentation — not added as an afterthought.
Bring us the constraint. We'll bring the team.
Most ETL failures are not dramatic. They are silent. A pipeline that loaded 10,000 records yesterday loads 9,847 today and nobody notices until the business analyst reconciles the report three weeks later and finds a discrepancy. Building observable pipelines — with record count tracking, schema drift detection, and freshness alerting — prevents this class of problem.
We build data pipelines for every latency tier: batch pipelines for overnight or intra-day data warehouse loads, streaming pipelines for near-real-time analytical use cases, and CDC-based pipelines for operational database replication.
Scheduled data extraction from source systems (databases, APIs, flat files), transformation to the target schema using dbt or custom transformation logic, and loading into BigQuery or other target systems. Pipeline orchestration via Cloud Composer (Apache Airflow) with proper DAG design: idempotent tasks, retry logic, dependency management, and alerting on failure or anomalous execution duration.
For use cases that require data in the warehouse within minutes of generation: Pub/Sub-based event ingestion, Dataflow streaming pipelines for transformation and deduplication, and streaming insert or BigQuery Storage Write API for low-latency warehouse loading.
Database-level CDC using Debezium or database-native log streaming for continuous replication from operational databases to BigQuery or other analytical targets. CDC is the appropriate pattern when the source system cannot support high-frequency API polling and the data must remain close to real-time.
SQL-based data transformation using dbt: model development with proper documentation, source freshness tests, and schema tests configured for every model. dbt introduces software engineering discipline to SQL transformations — version control, peer review, and automated testing — that ad-hoc query-based transformations can't support.
Pipeline tested with representative data volumes. Record count reconciliation between source and target. dbt schema tests and source freshness checks activated. Failure scenarios tested: source unavailable, schema change, duplicate records.
Pipelines deployed to production with monitoring dashboards and alert policies. Runbook for each pipeline covering: what it does, what it depends on, how to diagnose failures, and how to trigger backfills. Source code handed over with documentation.