Upload Custom Processors and UDFs for Enrichment and Normalization
To perform the above enrichment and normalization, upload the custom UDFs and processors you downloaded in the previous section.
Getting Started with Streaming Analytics
Also available as:
- 1. Building an End-to-End Stream Application
- 2. Prepare Your Environment
- 3. Creating a Dataflow Application
- 4. Creating a Stream Analytics Application
- Create a Service Pool and Environment
- Create Your First Application
- Creating and Configuring the Kafka Source Stream
- Connecting Components
- Joining Multiple Streams
- Filtering Events in a Stream using Rules
- Using Aggregate Functions over Windows
- Implementing Business Rules on the Stream
- Transforming Data using a Projection Processor
- Creating Alerts with Notifications Sink
- Streaming Alerts to an Analytics Engine for Dashboarding
- Streaming Violation Events to an Analytics Engine for Descriptive Analytics
- Streaming Violation Events into a Data Lake and Operational Data Store
- 5. Deploy an Application
- 6. Stream Operations
- 7. Advanced: Doing Predictive Analytics on the Stream
- Logistical Regression Model
- Export the Model into SAM's Model Registry
- Enrichment and Normalization of Model Features
- Setting up your Enrichment Store and Building Custom UDFs and Processors
- Upload Custom Processors and UDFs for Enrichment and Normalization
- Scoring the Model in the Stream using a Streaming Split Join Pattern
- Streaming Split Join Pattern
- Score the Model using the PMML Processor and Alert
- 8. Creating Visualizations Using Superset