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ETL Done Right Is More Than Moving Data

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.

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.

Batch ETL Pipelines

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.

Streaming Pipelines

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.

Change Data Capture

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.

dbt Data Transformation

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.