Explain how you approach data ingestion from multiple sources.
Multi-Source Data Ingestion Strategy
In a modern data stack, ingestion is more than just moving bits. It's about building a scalable, resilient, and observable pipeline that can handle everything from legacy SQL databases to real-time event streams.
1. Identification & Source Profiling
Before writing code, we categorize sources. Are they Structured (SQL), Semi-Structured (JSON/logs), or Unstructured (PDFs)? We assess the data volume and the required "freshness" (latency) to decide between Batch or Stream processing.
2. Selecting the Ingestion Pattern
We apply specific patterns based on the source:
- Change Data Capture (CDC): For databases, using tools like Debezium to stream row-level changes without overloading the source DB.
- API Pull/Push: Using Python or dedicated connectors (Airbyte/Fivetran) for SaaS platforms like Salesforce or Zendesk.
- Event Streaming: Using Apache Kafka or AWS Kinesis for real-time clickstream data.
3. Landing Zone & Schema Evolution
Data is first landed in a "Bronze" or Raw Zone (S3/Azure Data Lake) in its original format. We implement Schema Registry to handle evolution, ensuring that if a source adds a new column, our downstream pipelines don't break.
4. Orchestration & Monitoring
We use Apache Airflow or Dagster to manage dependencies. Observability is key: we track record counts, latency, and data quality (using Great Expectations) at the moment of entry.
Ingestion Strategy Matrix
| Source Type | Tooling | Frequency |
|---|---|---|
| Relational (PostgreSQL/MySQL) | Debezium / AWS DMS | Real-time (CDC) |
| SaaS APIs (Shopify/Salesforce) | Airbyte / Python Requests | Scheduled (Hourly/Daily) |
| Web/App Logs | Kafka / Fluentd | Streaming (Sub-second) |
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