Kafka Streams API for Developers Practice Exam

Kafka Streams API for Developers Practice Exam

Kafka Streams API is a tool that allows applications to process, analyze, and transform real-time data that flows through Apache Kafka. Think of it like a pipeline that can filter, combine, or react to continuous streams of information instantly, instead of waiting for the data to be stored. It helps businesses handle massive amounts of live data, such as tracking financial transactions, monitoring sensors, or analyzing customer activity on websites.

Unlike traditional systems that require storing data before processing, Kafka Streams lets applications respond to events as soon as they happen. This makes it powerful for industries that rely on real-time decisions, such as banking, e-commerce, logistics, and telecommunications. It also integrates easily with existing Java applications, making it developer-friendly.

Who should take the Exam?

This exam is ideal for:

• Data Engineers
• Software Developers
• Cloud Engineers
• Big Data Architects
• DevOps Engineers
• Data Scientists (working with real-time data)
• Technical Leads / Solution Architects

Skills Required

• Basics of Java programming
• Understanding of Apache Kafka fundamentals
• Knowledge of distributed systems and event-driven architecture
• Familiarity with data pipelines and streaming concepts

Knowledge Gained

• Building real-time applications using Kafka Streams
• Designing event-driven solutions
• Transforming, aggregating, and joining data streams
• Deploying and scaling streaming applications
• Applying fault tolerance and resilience in stream processing

Course Outline

The Kafka Streams API Exam covers the following topics -

1. Introduction to Apache Kafka

• Kafka basics and architecture
• Producers, consumers, and topics
• Partitions and replication

2. Understanding Kafka Streams

• What is Kafka Streams API
• Stream vs. Table concepts
• KStreams and KTables

3. Core Operations in Streams

• Filtering, mapping, and transforming data
• Joins between streams and tables
• Windowing and aggregations

4. State Management

• Local state stores
• Fault tolerance with RocksDB
• Querying state stores

5. Deployment and Scaling

• Running Kafka Streams applications
• Horizontal scaling and partition rebalancing
• Handling failures

6. Integration and Advanced Features

• Kafka Connect and Streams
• Interactive queries
• Global tables and repartitioning