Data analytics pipelines on GCP leverage fully managed services to ingest, process, store, and visualize data at scale. This template diagrams a production pipeline using Cloud Pub/Sub for real-time ingestion, Dataflow (Apache Beam) for stream and batch processing, BigQuery as the analytics data warehouse, and Looker for dashboards and reporting. Use it to document your data infrastructure or plan a new analytics platform.
Anatomy of a GCP Data Pipeline
A typical GCP data pipeline follows four stages: ingestion, processing, storage, and visualization. Data enters through Pub/Sub (streaming) or Cloud Storage (batch), gets transformed by Dataflow or Dataproc, lands in BigQuery for analytics, and is surfaced through Looker or Data Studio dashboards. This template maps each stage to specific GCP services.
Streaming vs. Batch Processing
The template shows both streaming and batch data paths. Streaming data flows through Pub/Sub into Dataflow for real-time transformations, while batch data is uploaded to Cloud Storage and processed on a schedule. Both paths converge in BigQuery, giving analysts a unified view of real-time and historical data.
Data Governance and Quality
The diagram includes optional nodes for Data Catalog (metadata management) and Dataplex (data quality and governance). These services help ensure your pipeline produces trustworthy, well-documented data assets that comply with organizational policies.