The Oracle GoldenGate Stream Analytics (GGSA) runtime component is a complete solution platform for building applications to filter, correlate, and process events in real-time. With flexible deployment options of stand-alone Spark or Hadoop-YARN, it proves to be a versatile, high-performance event processing engine. GGSA enables Fast Data and Internet of Things (IOT) – delivering actionable insight and maximizing value on large volumes of high velocity data from varied data sources in real-time. It enables distributed intelligence and low latency responsiveness by pushing business logic to the network edge.
Key features of GGSA:
- Natively integrated with Oracle GoldenGate to process and analyze transaction streams from relational databases
- Interactive pipeline designer with live results to instantly validate your work
- Zero-code environment to build continuous ETL and analytics workflows
- Pattern library for advanced data transformation and real-time analytics
- Extensive support for processing geospatial data
- Secured connectivity to diverse data sources and sinks
- Built-in support for real-time visualizations and dashboards
- Automatic application state management
- Automatic configuration of pipelines for high availability and reliability
- Automatic configuration of pipelines for lower latency and higher throughput
- Automatic log management of pipelines for better disk space utilization
1.1 GGSA Architecture Overview
- GoldenGate: Natively integrated with Oracle GoldenGate, Stream Analytics offers data replication for high-availability data environments, real-time data integration, and transactional change data capture.
- Oracle Cloud Streaming: Ingest continuous, high-volume data streams that you can consume or process in real-time.
- Kafka: A distributed streaming platform used for metrics collection and monitoring, log aggregation, and so on.
- Java Message Service: Allows java-based applications to send, receive, and read distributed communications.
With Stream Analytics, you can filter, correlate, and process events in real-time.
- Oracle Cloud Streaming
- Java Message Service
Learn more about Managing Targets
1.2 Steps to build Continuous-ETL and Realtime-Analytics Pipelines
|Step 1||Create a Connection||
You must create a connection to an external system, to be
Supported stream sources:
See Managing Connections.
|Step 2||Create a Stream||
From the Catalog, create a Stream using the connection from Step 1.
Supported stream definitions:
See Managing Streams.
|Step 3||Create a Pipeline||
From the Catalog, create a Pipeline using the stream from Step 2.
|Step 4||Add Business Logic||
Add business logic to the pipeline to analyze the input data stream.
|Step 5||Publish the Pipeline||See Publishing a Pipeline|