Roadmap for Streaming Analytics Service on IBM Cloud
IBM Streaming Analytics is available on IBM Cloud (www.bluemix.net). Streaming Analytics is built upon the IBM Streams technology. Streams is an advanced analytic platform allowing user-developed applications to quickly ingest, analyze, and correlate information as it arrives from a wide variety of real-time sources. The Streaming Analytics service gives you the ability to deploy Streams applications to run in the IBM Cloud. This roadmap helps you get started and learn about the IBM Streaming Analytics Service.
Step 1 – Learn about Streams
If you are new to Streams, please take some time to learn about the Streams technology. If you already have experience with Streams, please move to the next step.
- Video: Learn Streams in 5 minutes
- Video: Build Streaming Applications
- Streams Quickstart Guide
Step 2 – Learn about the Streaming Analytics Service
Please read this quick introduction to the IBM Cloud Streaming Analytics Service
Step 3 – Run a Sample
There are a number of sample applications you can use to learn about the Streaming Analytics service. Please run one or more of the samples below that most closely match your intended use of Streaming Analytics service in IBM Cloud.
Deploy the stock trades starter application
This is a simple application that is ready to be deployed to the Streaming Analytics service. You do not have to write any code or install any software.
To try it, download the application file from GitHub, and then watch this video to see how to:
- Create an instance of the Streaming Analytics Service (00:22)
- Run the starter application in the service (00:56)
- See the actual data being processed by an application (02:13)
- Look at the applications logs and any data printed to stdout/stderr (03:17)
Streams Application Samples
Run Streams application samples to simply explore how to run Streams apps in the IBM Cloud. Use the project link below to access a number of sample applications written in SPL that are ready to submit to your Streaming Analytics instance in IBM Cloud. Consult the PDF file in the project for instructions about how to run and explore the samples.
End-to-End IBM Cloud Starter Applications
Run an end-to-end IBM Cloud starter application if you want to explore how to use Streaming Analytics in the context of a IBM Cloud application that uses a IBM Cloud languages runtime and one or more IBM Cloud services.
The EventDetection starter application uses the SDK for Node.js runtime in IBM Cloud. EventDetection uses the Streaming Analytics service to detect simple and complex events in a Stream of real-time weather data.
The NYCTraffic starter application uses the Liberty for Java runtime in IBM Cloud. NYCTraffic uses the Streaming Analytics service to analyze a real-time feed of road speeds.
Step 4 – Learn More about Developing Streams Applications for the Cloud
See the IBM Cloud Streaming Analytics Development Guide for a comprehensive description of how to develop and deploy applications to the Streaming Analytics service. The development guide features a sample Streams application that analyzes Twitter data and step-by-step instructions for completing tasks that are part of the application development life cycle.
If you have an existing Streams application that you would like to deploy to the cloud, see Getting your SPL application ready for the cloud.
Step 5 – Learn about Integrating with other IBM Cloud Services
A variety of tutorials are available to help you use the Streaming Analytics service with other IBM Cloud services. All of the integration tutorials include code samples.
Watson IoT / Internet of Things Platform
File-based Data Sources
Step 6 – Taking Care of your Streams Application in the Cloud
If you have a Streams application running in the IBM Cloud Streaming Analytics service, its most likely one that runs continuously – 24 hours a day and 7 days a week. But how can you make sure that your application is running normally? See Monitoring Your Streams App in IBM Cloud using the Streams REST API to learn how you can programmatically assess whether your app is operating normally and extract valuable metrics about its execution.