Problem Statement
Organisations integrating data from multiple external sources — APIs, flat files, partner feeds, and database exports — often end up with fragile, one-off scripts that break silently when source schemas change or upstream services are unavailable. The goal was a structured ETL framework that enforces data quality at the ingestion boundary, handles failures gracefully with retries, and provides operational visibility so data teams can catch and resolve issues before they corrupt downstream analytics.
Key Challenges:
- Heterogeneous source formats — REST APIs, CSV, XML, database dumps
- Schema drift from upstream providers breaking downstream pipelines
- Silent failures from flaky source connectivity propagating to analytics
- Scaling to process large datasets within scheduling windows
- Operational visibility without requiring complex infrastructure
System Architecture
Pipelines are defined as composable Python classes with standardised interfaces for extraction, validation, transformation, and loading. Celery workers execute scheduled and on-demand pipeline runs; Redis tracks state. A web-based operations dashboard displays run history, failure details, and data quality metrics.
Pipeline Abstractions
Base classes for Extractor, Validator, Transformer, and Loader define clear interfaces. New integrations are built by subclassing and filling in source-specific logic, with the framework handling scheduling, retries, checkpointing, and monitoring automatically.
Validation Layer
Schema declarations define expected field types, nullability, value ranges, and referential constraints. Every ingested record is validated before transformation; invalid records are quarantined with error details rather than silently dropped or passed through.
Scheduling & Execution
Celery Beat manages scheduled pipeline triggers; on-demand runs are dispatched via the API. Worker concurrency is configurable per pipeline, enabling large pipelines to run in parallel across worker nodes.
Monitoring & Alerting
Each run logs start time, duration, record counts (processed / valid / rejected), and failure details to PostgreSQL. Dashboards surface trends, and alert rules notify on failure rates or volume anomalies exceeding thresholds.
Key Engineering Challenges
Schema Drift Handling
Challenge: Upstream providers add, rename, or remove fields without notice, silently breaking transformations.
Solution: Forward-compatible schema declarations that tolerate unknown extra fields while enforcing required fields, combined with schema change detection alerts notifying the team when upstream structure diverges.
Quarantine Without Data Loss
Challenge: Rejecting invalid records at the boundary prevents corruption, but discarding them loses potentially recoverable data.
Solution: Invalid records are written to a quarantine table with full context (raw input, validation errors, run ID) enabling manual review, correction, and re-ingestion without re-fetching from the source.
Exactly-Once Processing
Challenge: Retried pipeline runs risk inserting duplicate records into the destination database.
Solution: Idempotent upsert operations using primary key conflict resolution in PostgreSQL, with run-level checkpointing enabling safe resumption from the last successful batch.
Large Dataset Performance
Challenge: Some source feeds contain hundreds of thousands of records that are slow to process row-by-row.
Solution: Batch processing with configurable chunk sizes, PostgreSQL COPY for bulk loads, and parallel worker execution across partitioned data slices.
Solutions Implemented
- Composable Pipeline Classes: Standardised Extractor / Validator / Transformer / Loader interfaces enabling rapid development of new source integrations.
- Schema-Declared Validation: Type, nullability, range, and referential checks enforced at ingestion boundary with quarantine for rejected records.
- Idempotent Upserts: Safe re-runs and retries without duplicate records using PostgreSQL conflict handling strategies.
- Run History Logging: Structured logs per pipeline run with record counts, timing breakdowns, and failure details queryable for operational analysis.
- Alert Rules: Configurable thresholds for failure rates, record volume drops, and processing time anomalies with multi-channel notifications.
Outcome & Impact
All errors surfaced and logged
Safe to retry any pipeline
New sources via framework patterns
Every record traceable to source run