Backfill-Safe Incremental Ingestion

Overview

Designed and implemented a backfill-safe incremental ingestion framework for analytics workloads, enabling reliable historical reprocessing without data duplication or full table reloads. The system supports late-arriving updates, selective recomputation, and predictable SLAs while minimizing warehouse cost.

Challenge

Analytics pipelines often require historical reprocessing due to late-arriving corrections, schema changes, or updated business logic. Naive backfills can double-count records, violate downstream consistency, and cause large, unnecessary compute spikes. The challenge was to enable safe reprocessing while preserving freshness guarantees and cost efficiency.

Solution

Built a unified incremental + backfill architecture with the following design:

• Incremental ingestion: Range-based loads driven by event time and updated_at watermarks to capture both new events and late-arriving changes.
• Backfill strategy: Windowed, partition-aware reprocessing that runs alongside daily loads, recomputing only affected time ranges.
• Data correctness: Idempotent writes using primary-key deduplication and MERGE semantics to prevent duplication during retries and historical rebuilds.
• Cost control: Partitioning and clustering to limit scanned data and avoid full table rewrites during backfills.
• Orchestration & observability: DAG-driven execution with checkpointed progress, backfill tracking, and anomaly detection.

Technical Implementation

Core components and tooling used in the pipeline:

• Python services to compute incremental and backfill ranges
• Google BigQuery with partitioned and clustered analytical tables
• dbt incremental models using MERGE logic for late-arriving data
• Apache Airflow DAGs for daily incrementals and targeted backfill workflows
• Cloud Run for stateless, scalable ingestion workers