#GeodeSummit - Integration & Future Direction for Spring Cloud Data Flow & Geode
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The document discusses the integration of Spring Cloud Data Flow with Apache Geode for managing stream and batch data processes. It emphasizes the need for low latency, high throughput, and efficient data handling in data pipelines. Key features mentioned include a drag-and-drop UI, security, and various configurations for processing data in real-time.
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#GeodeSummit - Integration & Future Direction for Spring Cloud Data Flow & Geode
- 1. Spring Cloud Data Flow + Geode Sabby Anandan | Product Manager | @sabbyanandan
- 2. Stream Batch Spring Cloud Data Flow Spring Cloud Stream Spring Cloud Task Shell; DSL; REST- APIs Drag & Drop UI Security OOTB Connectors Reactive Data Science
- 3. Dataflow Server Admin / Flo UI Shell CURL ??X Stream/Task Spring Boot Apps YARN
- 4. Why are we here? Data Pipelines requiring: • Low latency and in-memory processing • High Throughput SLAs • Correlation between reference-data and data- in-flight • Frequent data-shuffling
- 5. http | transform | log
- 6. | = ? http | transform | log
- 7. | = Binder
- 8. Binders
- 9. Region Data Buckets http | transform | log Geode Cluster transform-processor.jar PARTITION transform-processor.jar PARTITION log-sink.jar PARTITION log-sink.jar PARTITION http-source.jar PARTITION_PROXY http-source.jar PARTITION_PROXY
- 10. What’s next? •+/- scaling and automatic re-partitioning •Stream / Task metadata-repository •Key-Value store for OOTB Counters •Partition level local-state and SQL-like stream processing
- 11. 11 Join the Apache Geode Community! • Check out http://geode.incubator.apache.org • Subscribe: [email protected] • Download: http://geode.incubator.apache.org/releases/