Design Automated Ingestion Governance Lifecycle Pipeline

What this solution is

This architecture combines Dataflow for unified stream/batch data processing, Cloud Composer for workflow orchestration, Dataplex for decentralized data governance and automated quality validation, and native BigQuery and Cloud Storage lifecycle management policies for compliance and data retention.

How it meets all requirements

• Ingestion and Transformation: Dataflow processes both unbounded streaming clickstream records and daily batch files. Implementing a dead-letter / branching pattern allows Dataflow to validate data contracts and route malformed records into dedicated BigQuery failure tables for forensic debugging while valid records flow cleanly to curated layers.
• Orchestration: Cloud Composer (managed Apache Airflow) is the standard Google Cloud tool for orchestrating complex operational workflows with multi-step dependencies, cross-service task triggers, and CI/CD-driven ETL pipelines.
• Quality and Governance: Dataplex creates logical lakes and zones across distributed Cloud Storage buckets and BigQuery datasets, utilizing auto data quality scans to enforce standardized validation rules in place without data consolidation.
• Lifecycle Management: BigQuery default partition and table expiration settings combined with Cloud Storage Object Lifecycle Management rules automate data deletion and archival after 90 days.

Key advantages

• Unified stream and batch semantics using Apache Beam in Dataflow.
• Comprehensive governance without centralizing physical storage through Dataplex Universal Catalog.
• Automated compliance by delegating deletion and tiering directly to native storage lifecycle mechanisms.
• Resilience and auditability by isolating failed records into dedicated forensic tables.

Why this is optimal

This approach aligns with Google Cloud reference architectures for enterprise data platforms, ensuring scalability, low operational overhead, robust data governance, and automated lifecycle compliance.