A well-designed data pipeline architecture ensures data flows reliably from source to destination, ready for analysis and operations.
Architecture Patterns
Batch Processing
- Process data in scheduled runs (hourly, daily)
- Good for large volumes where latency is acceptable
- Simpler to build and maintain
- Tools: Apache Spark, Airflow, dbt
Stream Processing
- Process data as it arrives, near real-time
- Good for low-latency requirements (fraud, alerts, dashboards)
- More complex, requires specialized tools
- Tools: Kafka, Flink, Kinesis
Lambda Architecture
- Combines batch and stream
- Batch layer for historical accuracy
- Speed layer for real-time results
- Serves merged views
Kappa Architecture
- Everything is streaming
- Simpler, one path for all data
- Requires replayable message log (Kafka)
Core Components
Data Sources
- Databases (PostgreSQL, MySQL, MongoDB)
- SaaS APIs (Salesforce, HubSpot, Stripe)
- Event logs (clickstream, application logs)
- Files (CSV, JSON, Parquet)
Ingestion Layer
- CDC (Change Data Capture). Stream database changes
- API connectors. Poll SaaS APIs on schedule
- Message queue. Buffer incoming events
Storage Layer
- Data lake. Raw, all data, cheap storage (S3, GCS)
- Data warehouse. Structured, queryable (Snowflake, BigQuery)
- Lakehouse. Both in one (Databricks, Delta Lake)
Processing Layer
- Transformation. Clean, join, aggregate
- Enrichment. Add reference data
- Quality checks. Validate and flag issues
Serving Layer
- BI tools. Looker, Tableau, Power BI
- Application APIs. Serve data to apps
- ML models. Feed features to models
- Reverse ETL. Push insights back to operational tools
Design Principles
- Idempotent operations. Running twice produces the same result
- Schema evolution. Handle source schema changes gracefully
- Lineage tracking. Know where each field comes from
- Monitoring. Alert on freshness, volume, and quality
- Cost awareness. Processing and storage costs can grow fast
How Switch 2 One Helps
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