Introduction to GoldenGate Stream Analytics

1 Introduction to GoldenGate Stream Analytics

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

GGSA Architecture

Acquiring data

Stream Analytics can acquire data from any of the following on-premises and cloud-native data sources:

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.

Processing data

With Stream Analytics, you can filter, correlate, and process events in real-time.

Perform actions on the data

After Stream Analytics processes the data, you can output the results to any one of the following external target data sources:

Coherence
Kafka
Oracle Cloud Streaming
Java Message Service
Database
Notification
REST

Learn more about Managing Targets

1.2 Steps to build Continuous-ETL and Realtime-Analytics Pipelines

Step	Action	Description
Step 1	Create a Connection	You must create a connection to an external system, to be Supported stream sources: Kafka OCI Streaming Service JMS Oracle Advanced Queuing See Managing Connections.
Step 2	Create a Stream	From the Catalog, create a Stream using the connection from Step 1. Supported stream definitions: File Kafka JMS AQ GoldenGate See Managing Streams.
Step 3	Create a Pipeline	From the Catalog, create a Pipeline using the stream from Step 2. See Creating a Pipeline
Step 4	Add Business Logic	Add business logic to the pipeline to analyze the input data stream. See Creating a Pipeline to Transform and Analyze Data Streams.
Step 5	Publish the Pipeline	See Publishing a Pipeline