Apache Kafka Training
Participants in Apache Kafka Training gain a thorough understanding of Kafka, a distributed streaming platform used to create streaming applications and real-time data pipelines. In addition to providing practical experience in managing Kafka clusters and creating Kafka-based applications, the course covers Kafka architecture, setup, configuration, and integration with other systems.
- 10+ Courses
- 30+ Projects
- 400 Hours
Apache Kafka Training is suitable for the following target audiences:
Data Engineers: Data engineers are professionals who manage and process massive amounts of data in real-time and want to use Kafka to create data pipelines.
Software developers: Developers who want to create event-driven systems and stream data in real time by incorporating Kafka into their applications.
System Administrators: IT managers in charge of setting up, configuring, and managing Kafka clusters in a company’s infrastructure.
Big Data Analysts: These analysts work with data lakes and require real-time streaming skills to process and evaluate data on time.
DevOps Engineers: DevOps engineers are specialists in automating Kafka cluster deployment and scaling while maintaining system dependability.
Kafka Engineer: Kafka engineers specialize in building, maintaining, and optimizing Kafka clusters for real-time data processing.
Data Engineer: Building real-time data pipelines that gather, process, and analyze massive amounts of data using Kafka.
Software Developer (Kafka): Creating apps that use Kafka’s event-driven architecture and real-time data streaming.
System Administrator (Kafka): The system administrator manages the Kafka infrastructure and guarantees its security, scalability, and performance.
Big Data Architect: Developing and putting big data solutions that use Kafka for streaming and data ingestion into practice.
Kafka expertise is becoming increasingly necessary for processing real-time data streams in industries including technology, healthcare, e-commerce, and finance. These sectors provide competitive pay and growth prospects in the USA and Canada.
- Introduction to event streaming and use cases
- Kafka core concepts: brokers, topics, partitions, offsets
- Kafka architecture and cluster components
- ZooKeeper vs KRaft mode overview
- Kafka ecosystem overview
- Kafka installation and local setup
- Hands-on: Install and configure Kafka and create topics and explore partitions. Produce and consume messages using CLI
- Producer architecture and message flow
- Consumer groups and rebalancing
- Offset management and commit strategies
- Message serialization formats
- Delivery semantics: at-most-once, at-least-once, exactly-once
- Practice: Java or Python producers and consumers , Test consumer group behavior, Experiment with offset commits
- Topic configuration and partition strategy
- Replication, ISR, and fault tolerance
- Log segments and retention policies
- Compression and batching
- Throughput vs latency tuning
- Practice Task: Configure replication and retention. Simulate broker failure and recovery. Performance testing and tuning exercises
- Kafka Connect architecture
- Source and sink connectors
- Standalone vs distributed mode
- Schema management basics
- Integrating Kafka with databases and filesystems
- Assignment :Set up Kafka Connect . Configure JDBC source and sink connectors. Stream data between Kafka and a database
- Stream processing concepts
- Kafka Streams architecture
- KStream and KTable
- Stateful vs stateless processing
- Windowing and joins
- Hands-on: Build a Kafka Streams application , Implement filtering, aggregation and joins , Process real-time data streams
- Introduction to Schema Registry
- Avro, JSON and Protobuf formats
- Schema evolution and compatibility
- Handling serialization errors
- Best practices for schema design
- Hands-on: Configure Schema Registry , Produce and consume Avro messages .Test schema evolution scenarios
- Authentication and authorization
- SSL, SASL and ACLs
- Kafka monitoring metrics
- Lag monitoring and alerting
- Backup, disaster recovery, and upgrades
- Hands-on :Configure security settings , Monitor Kafka using built-in tools. Troubleshoot common production issues
- Case studies from industry
- End-to-end project development
- Capstone Project: Build a real-time data streaming pipeline using Kafka, Kafka Connect and Kafka Stream
- Mock Interviews & Job Placement
Apache Kafka is used for real-time data streaming, event-driven architectures, log aggregation, and building scalable, fault-tolerant data pipelines.
Basic programming knowledge and a general understanding of distributed systems are recommended. Prior experience with Java, Python, or cloud platforms is helpful but not required.
The duration is 2 months (8 weeks), with sessions held 2 times per week (either during week or weekends), including theory, hands-on practice and project work.
Yes, upon successful completion, you’ll receive a Certificate of Completion from Checkmate IT Tech.
We offer online training classes to promote easy access to all candidates. Recordings are also made available for revision or if you miss a session.
Yes. We provide resume reviews, mock interviews, LinkedIn optimization, and guidance on job portals to help boost your chances in the job market.
The course covers both. You will learn Kafka producers, consumers, Streams, and Connect, along with operational topics like monitoring, security, and performance tuning.
Kafka core, Kafka Connect, Kafka Streams, Schema Registry, and Kafka security and monitoring concepts are all covered.
The course explains both, with a focus on modern Kafka deployments using KRaft, while still covering ZooKeeper for legacy understanding.
Yes, the core curriculum focuses on Kafka fundamentals and architecture. Cloud deployment concepts are discussed and optional extensions can include AWS or Azure setups.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
Email info@checkmateittech.com OR Call Us +1-347-4082054
- Submit Form
Job opportunities in USA and Canada
Kafka Engineer: Kafka engineers specialize in building, maintaining, and optimizing Kafka clusters for real-time data processing.
Data Engineer: Building real-time data pipelines that gather, process, and analyze massive amounts of data using Kafka.
Software Developer (Kafka): Creating apps that use Kafka’s event-driven architecture and real-time data streaming.
System Administrator (Kafka): The system administrator manages the Kafka infrastructure and guarantees its security, scalability, and performance.
Big Data Architect: Developing and putting big data solutions that use Kafka for streaming and data ingestion into practice.
Kafka expertise is becoming increasingly necessary for processing real-time data streams in industries including technology, healthcare, e-commerce, and finance. These sectors provide competitive pay and growth prospects in the USA and Canada.
