Overview of Data Pipelines Integration Patterns
The patterns provided below provide support for batch, mini-batch (near-time), and real-time data integration processing. While they support virtually any enterprise integration architecture individually or in combination such as the Remote Site Replication architecture, using both mini-batch and real-time streaming data capture together can enable other well-known architectures such as the Lambda Architecture and the Kappa architecture.
The patterns presented here are divided in three main groups: stateless, stateful, and composite.
- Stateless: Pushes data from one system to another in its simplest form
- Statefull: Pushes data from one system to another selectively using a watermark or a synthetic change capture mechanism
- Composite: Combines two or more patterns previously defined, possibly mixing stateless and stateful patterns to provide the desired integration outcome
| Stateless Patterns | Snapshot |  |
Captures the entire data set from the source system, with or without a filter Learn more... |
| Hook |  |
Forwards data that was received by a listener Learn more... | |
| CDC Stream |  |
Forwards data provided by a native CDC engine Learn more... | |
| Stateful Patterns | Watermark |  |
Forward-only read mechanism that keeps track of a high watermark value Learn more... |
| CDC |  |
Synthetic operation that reads source records and only pushes data that changed Learn more... | |
| Window |  |
Read operation that goes back in time to recapture previously captured data Learn more... | |
| Composite Patterns (partial list) |
Watermark + CDC |  |
Forward-only read mechanism that keeps track of a high watermark value and filters out records that were previously captured Learn more... |
| One-Way Sync |  |
Integration strategy that keeps a source system synchronized with one or more target systems Learn more... | |
| Two-Way Sync |  |
Integration strategy that keeps two or more systems synchronized, but data can be updated in any system | |
| Aggregation |  |
Centralizing data from multiple sources or multiple locations to build data lakes, data hubs, or enable
advanced analytics, reporting, or AI/ML scenarios Learn more... |