System integration
System integration is the process of connecting or combining software components or systems so they can exchange data or functionality and operate together.
Kafka for Architects
Integration is the coordination and interconnection of separate systems so they can exchange data or functionality.
- How it is used
- Coordinates enterprise data exchange, often using Kafka as a central streaming hub.
- How deep it goes
- Explains contracts, connectors, transformations, governance, and trade-offs, while stopping short of client-API implementation.
- Examples
- Uses a Customer 360 data store, JDBC source connector, database transactions, incrementing IDs, and the TRANSACTIONS topic.
- What you can do after
- Evaluate integration designs and apply Kafka Connect to move governed data between enterprise systems.
Platform Engineering on Kubernetes
The connection and coordination of separate software systems so they operate together as part of a larger system.
- How it is used
- Supports platform workflows and delivery measurement by coordinating tools, services, data, and organizational practices.
- How deep it goes
- Explains integration goals, data aggregation, and standardization but defers concrete implementation details to event standards.
- Examples
- Uses CRM systems, Four Keys, LinearB, CloudEvents, and CDEvents as integration examples.
- What you can do after
- Diagnose delivery bottlenecks by aggregating information from multiple systems into extensible performance metrics.
Read how Platform Engineering on Kubernetes explains system integration →
Generative AI in Action
The process of connecting software components or systems so they operate together and exchange capabilities or data.
- How it is used
- Supports production deployment by fitting generative AI models into established systems, workflows, governance processes, and operational controls.
- How deep it goes
- Frames integration as a production checklist concern and discusses compatibility and coordination without specifying an integration architecture or implementation.
- Examples
- Uses existing workflows, API management, quota controls, retry logic, MLflow, Traceloop, and Prompt flow as operational integration points.
- What you can do after
- Operate/configure generative AI deployments that fit existing workflows while accounting for reliability, oversight, security, compliance, and cost.
Read how Generative AI in Action explains system integration →
Related concepts
- process-oriented botused by · A process-oriented bot is a conversational agent that guides a user through a sequence of questions or actions to achieve a goal or complete a transaction.
- xmlused by · XML is a text-based markup language for encoding structured documents and data using elements, attributes, and nested relationships.
- deployment pipelineused by · A sequence of automated or manual stages that builds, tests or validates, packages, and delivers software or models toward deployment.
- Apache Kafkaused by · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
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