What is a Data Pipeline?
Understand data pipelines — what they are, how they work, and how NoSQLSync's CDC pipeline keeps MongoDB, DynamoDB, and Firestore in sync with SQL databases.
What is a Data Pipeline?
A data pipeline is a series of steps that move data from one system to another, transforming it along the way. In its simplest form: Extract data from a source, Transform it (clean, map types, filter, enrich), and Load it into a target. More sophisticated pipelines add real-time streaming, error handling, monitoring, and exactly-once delivery guarantees.
Batch vs Real-Time Pipelines
Batch pipelines process data in chunks on a schedule — every hour, every day. They're simpler to build but have inherent latency. Real-time pipelines process each record as it arrives, within milliseconds. They're more complex but essential when downstream systems need fresh data.
The NoSQLSync Migration Pipeline
NoSQLSync's pipeline has multiple stages: Schema Discovery (sample the source, infer the schema), Schema Mapping (detected fields → target columns with type mapping), Bulk Load (parallel workers migrate historical data at high throughput), CDC Sync (real-time change capture via oplog/streams), Progress reporting (records migrated vs. records total), and Cutover (switch application reads/writes to target).
Error Handling in Data Pipelines
Migration pipelines face several failure modes: type mismatches (a field that was a string suddenly appears as an integer), network interruptions, source database restarts, schema changes mid-migration. NoSQLSync handles these with retry logic, dead-letter queues for unprocessable records, real-time alerts, and pause/resume capability.
Monitoring Your Pipeline
Production pipelines need observability: records processed per second, latency from source write to target write, error rates by collection/table, pipeline lag (how far behind is the CDC stream?), and validation discrepancies. NoSQLSync provides a real-time dashboard with all these metrics.