> ## Documentation Index
> Fetch the complete documentation index at: https://docs.streemlined.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> Build Kafka apps as you’d draw them on a whiteboard
Streemlined is a stream processing engine designed to be simple, reliable, and efficient. It focuses on the **80% of use cases** that matter most for event-driven applications.
Build pipelines visually with a modern drag-and-drop editor
Integrates seamlessly with the Kafka Connect ecosystem
Built-in monitoring, error handling, and health checks
No central coordinator. Each instance runs independently
## Streemlined in 60 seconds
## Where does Streemlined fit the ecosystem?
Kafka Streams and Spring Kafka give teams autonomy, but demand Java expertise. Shared Kafka Connect clusters are no-code, but centralized and dependent on a platform team.
**Streemlined is both**: the simplicity of Connect with the independence of Streams, letting any team own their pipelines end-to-end.
## When to Use Streemlined
Streemlined shines when you need to move fast and ship reliable pipelines without a dedicated streaming team:
* **Fan-out to multiple sinks** — one topic, many destinations: databases, search indexes, warehouses, APIs
* **Real-time enrichment** — hydrate events with lookups from HTTP services or caches before they land
* **State-store** — maintain key-value state across events for deduplication, caching, or lightweight aggregations
* **Smart routing** — split, filter, and route messages based on content to different topics or systems
* **Protocol bridging** — connect Kafka to anything with REST, JDBC, or the 200+ Kafka Connect plugins
* **Self-service streaming** — let product teams own their data flows without waiting on platform engineers
## When to prefer other options
Streemlined deliberately trades off advanced stateful capabilities for simplicity. If your use case requires any of the following, consider Kafka Streams or Flink:
* **Windowed aggregations** — time-based grouping, tumbling/sliding windows, session windows
* **Stream-to-stream joins** — correlating events across multiple topics by key and time
* **Exactly-once across multiple outputs** — transactional writes spanning Kafka and external systems
## Next Steps
Build your first pipeline in 10 minutes
Understand pipelines, nodes, and JSONata
## When to Use Streemlined
Streemlined shines when you need to move fast and ship reliable pipelines without a dedicated streaming team:
* **Fan-out to multiple sinks** — one topic, many destinations: databases, search indexes, warehouses, APIs
* **Real-time enrichment** — hydrate events with lookups from HTTP services or caches before they land
* **State-store** — maintain key-value state across events for deduplication, caching, or lightweight aggregations
* **Smart routing** — split, filter, and route messages based on content to different topics or systems
* **Protocol bridging** — connect Kafka to anything with REST, JDBC, or the 200+ Kafka Connect plugins
* **Self-service streaming** — let product teams own their data flows without waiting on platform engineers
## When to prefer other options
Streemlined deliberately trades off advanced stateful capabilities for simplicity. If your use case requires any of the following, consider Kafka Streams or Flink:
* **Windowed aggregations** — time-based grouping, tumbling/sliding windows, session windows
* **Stream-to-stream joins** — correlating events across multiple topics by key and time
* **Exactly-once across multiple outputs** — transactional writes spanning Kafka and external systems
## Next Steps