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Learning path: Get started with IBM Streams

This learning path is designed for anyone interested in quickly getting up to speed with IBM Streams — an advanced analytics platform that allows you to develop applications that analyze data in real time. With IBM Streams, you can ingest and correlate data from thousands of sources in real time; continuously analyze data with low-latency response times; and create apps that score machine learning models in real time, allowing you to detect patterns and trends as they occur. And IBM Streams apps can be created with popular languages like Java, Python and C++.

To get started, click on a card below.

Introduction to IBM Streams

Learn about:

  • Key advantages of using IBM Streams
  • Terms and concepts
  • Use cases
  • Tooling

Create your first IBM Streams app without writing code

Learn about:

  • Building and demonstrating a streaming app
  • Creating a new project
  • Rebuilding the app

Ingest data from Apache Kafka

Learn about:

  • Creating a streaming app powered by Apache Kafka
  • Using IBM Event Streams on IBM Cloud

Build a streaming app using a Python API

Learn about:

  • Creating a Jupyter Notebook
  • Using the streamsx Python API
  • Viewing events

Access streaming data with REST services

Learn about:

  • Adding a web service to a streaming app
  • Posting and getting data
  • Testing from Python and Swagger

Score streaming data with a machine learning model

Learn about:

  • Building and deploying a machine learning model
  • Creating and running an IBM Streams application