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- Cloud
- Data
Stream Processing Foundations
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. The planned content for this path includes the following: - Stream Processing Core Concepts (video course) - Understanding Events, Time, and Windows in Stream Processing (video course) - Understanding State and Fault Tolerance in Stream Processing (video course) - Stream Processing Frameworks: Apache Kafka and ksqlDB (video course) - Stream Processing Frameworks: Apache Flink and Apache Beam (video course) - Stream Processing Frameworks: Apache Spark Structured Streaming (video course) - Stream Processing Frameworks: Cloud-native Tools (video course) - Design Reliable Streaming Architectures (video course)
Stream processing is the continuous ingestion, processing, and analysis of data in real time as it flows through a system. This learning path introduces the core concepts, time and state management, and reliability challenges of stream processing before exploring today’s most widely used tools, including Apache Kafka, Flink, Beam, Spark Structured Streaming, and cloud-native services. You’ll also learn how to design resilient architectures that meet real-time data demands at scale.
Content in this path
Stream Processing Core Concepts
The courses in this section cover the foundational knowledge you need to get up and running with stream processing solutions. Here, you'll learn about key stream processing terminology and review topics like events, time, windows, state, and fault tolerance.
Stream Processing Frameworks
In this section of the path, you'll learn about the most popular and commonly-used stream processing frameworks in today's modern data workflows.
Stream Processing Architecture
In this section of the path, you'll learn how to design reliable streaming architecture.
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What You'll Learn
- Core concepts of stream processing
- How events, time, and windows work in stream processing
- How state and fault tolerance work in stream processing
- The concepts of different stream processing frameworks, including tools like Apache Kafka and Apache Flink
- How to design reliable streaming architectures
- Learners interested in this path should have a basic understanding of data systems and data pipelines. Familiarity with databases and SQL is also helpful, along with an awareness of modern data architecture concepts and patterns. Exposure to messaging systems or pub/sub patterns is also helpful, but not required.
- Stream Processing
- Apache Flink
- Apache Beam
- Data architecture
- Data streaming
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