Confluent Certified Developer for Apache Kafka || 2026 ||

Name: Confluent Certified Developer for Apache Kafka || 2026 ||
Rating: 4.7 (77 reviews)

Advanced Apache Kafka: Architecture, Streaming, and Real-Time Systems, Build High-Performance Data Pipelines for Success

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Created byMuhammad Naeem

Last updated 4/2026

English

English [Auto],

What you'll learn

Understand the fundamentals of event streaming and how Apache Kafka fits into modern data architectures
Learn Kafka core architecture, including brokers, topics, partitions, offsets, and replication
Gain knowledge of Kafka cluster components such as ZooKeeper and KRaft mode
Understand data durability, fault tolerance, and in-sync replicas (ISR)
Build and configure Kafka producers for real-world applications
Apply different message partitioning strategies and key distribution techniques
Master delivery semantics: at-most-once, at-least-once, and exactly-once
Handle retries, failures, and error scenarios in producers
Develop Kafka consumers and work with consumer groups effectively
Understand partition rebalancing and consumer group coordination
Learn offset management and committing strategies
Handle deserialization issues and problematic messages (poison pills)
Design and implement stream processing applications using Kafka Streams
Understand the difference between KStream and KTable and their use cases
Perform stateless and stateful stream operations
Understand streams vs. tables and push vs. pull queries in ksqlDB
Implement authentication, authorization, and role-based access control (RBAC)
Build scalable, fault-tolerant, and real-time data streaming systems
Gain practical skills to work with Kafka in production environments

Course content

5 sections • 25 lectures • 1h 46m total length

The Core Concepts of Event Streaming4:13
Explore how event streaming delivers real-time, durable data flows. See how producers, consumers, and the broker create a decoupled, immutable log that enables replay and massive scalability.
Kafka Cluster Architecture: Brokers, Zookeeper, and KRaft4:12
Explore how a Kafka cluster uses multiple brokers to provide high availability and scalability, with Zookeeper coordinating leadership and discovery, transitioning to KRaft for internal metadata management and faster failover.
Topics, Partitions, and Offsets4:12
Explore how topics, partitions, and offsets organize and scale data in a cluster, with append-only topics, parallel partitions for higher throughput, and offset-based consumer progress.
Replication and In-Sync Replicas (ISR)4:32
Explore how replication and in-sync replicas ensure availability in a Kafka cluster by distributing partition copies across brokers and managing leaders and followers.
The Role of the Confluent Schema Registry4:42
The Confluent Schema Registry enforces schema evolution and backward/forward compatibility, acting as a central authority that decouples schemas from messages for reliable, high-throughput event streaming.

Producer Architecture and the Message Lifecycle4:14
Explore the producer architecture, including serializer, registry, and partitioner, and how buffering, batching, and an asynchronous IO thread ensure high throughput and reliable broker acknowledgments.
Essential Producer Configuration Parameters3:59
Master essential producer configuration for data transmission and acknowledgments, including acks, retries, and delivery timeout. Explore batching, linger, batch size, compression, and idempotence to balance speed with data integrity.
Message Partitioning Strategies and Key Distribution4:30
Learn how Kafka distributes messages across partitions with sticky partitioning and keyed partitioning, uses Murmur2 hashing for deterministic ordering within a partition, and avoids hot partitions for scalable throughput.
Delivery Semantics: At-Most, At-Least, and Exactly-Once4:12
Compare at-most-once, at-least-once, and exactly-once delivery, explaining producer choices, acknowledgments, retries, idempotent producers, and transactions, for reliable, high-volume streaming and data integrity.
Handling Producer Retries and Error Scenarios4:20
Study how producers handle retriable and non-retriable errors and delivery timeout milliseconds limits retries. Observe idempotence preserves order amid retries, while a callback and a dead letter queue support observability.

Consumer Groups and Partition Rebalance Mechanics4:15
Explore how consumer groups divide topic partitions to scale data processing, understand partition rebalancing strategies from eager to incremental cooperative, and master heartbeats and timeouts.
Essential Consumer Configuration Parameters4:10
Master essential Kafka consumer parameters, including group id, auto offset reset (earliest or latest), enable auto commit, and key tuning knobs like fetch min bytes, max poll records, and timeouts.
Offset Management and Committing Strategies4:26
Master offset management to control when a message is considered complete in a data stream. Explore auto-commit risks, synchronous and asynchronous commits, and external offsets for exactly-once processing.
Consumer Poll Loop Mechanics and Threading Models4:04
Explore the poll loop powering a Kafka consumer with heartbeats, group coordination, backpressure via the poll duration, and how single-threaded operation and decoupled processing manage offsets, ordering, rebalance.
Handling Deserialization Errors and Poison Pills4:29
Design resilient consumption by wrapping deserializers to catch deserialization errors, log problematic bytes, and skip bad records. Use a dead letter queue with metadata and monitoring to maintain throughput.

Stream Processing Concepts: Topologies and Processors4:33
Explore the processor topology of Kafka Streams as a directed acyclic graph of source, stream, and sink processors, with state stores, tasks, and threads enabling scalable, real-time data processing.
KStream vs. KTable: Duality and Use Cases4:02
Explore the k-stream vs k-table duality in Apache Kafka, and learn how streams capture history while tables reflect the current state for real-time enrichment and dashboards.
Stateless Operations in Kafka Streams3:51
Explore stateless operations in Kafka Streams, processing each record in isolation with minimal latency. Apply filter, map, flat map, branch, and select key to shape data with high throughput.
Stateful Operations and State Stores3:59
Utilize stateful operations and local state stores to maintain context across events, enabling real-time insights with running totals and stream joins, while changelog topics with log compaction ensure fault-tolerant recovery.
Time Concepts and Windowing Strategies4:43
Master event time, ingestion time, and processing time to handle out-of-order data. Explore tumbling, hopping, and session windows for actionable streaming insights.

ksqlDB Architecture and Processing Model4:06
Explore ksqlDB architecture, a stream-processing specialized database built on Kafka Streams, enabling persistent SQL-like queries over live streams with scalable, fault-tolerant, real-time transformations, joins, and aggregations.
Streams vs. Tables in ksqlDB3:52
Distinguish streams and tables in ksqlDB, where streams are immutable event histories and tables reflect the current state via upserts. Use them together to build real-time, SQL-based analytical pipelines.
Push vs. Pull Queries in ksqlDB4:10
Explore how push queries provide continuous updates in a streaming database with sub-second latency, while pull queries deliver fast, point-in-time state from local stores, enabling a hybrid end-to-end experience.
Confluent Security Concepts: Authentication and Authorization4:39
Establish a secure foundation with authentication via SASL and Kerberos, then enforce authorization with ACLs and RBAC to uphold least privilege, and protect data in transit with TLS.
Role-Based Access Control (RBAC) in the Confluent Ecosystem4:14
Apply role-based access control across the Confluent ecosystem with hierarchical scoping and standardized roles like System Admin, User Admin, and Developer to manage cluster, environment, and topic permissions.

Requirements

No prior experience with Apache Kafka is required — this course starts from the fundamentals and gradually moves to advanced topics

Description

This course contains the use of artificial intelligence.

This is an Unofficial Course.

This comprehensive course is designed to take you from a complete beginner to a confident, job-ready professional in Apache Kafka and real-time data streaming. In today’s data-driven world, organizations rely heavily on event-driven architectures to process massive volumes of data in real time, and Apache Kafka has become the industry standard for building scalable, fault-tolerant streaming platforms. This course provides a deep and practical understanding of Kafka’s core architecture, development workflows, and advanced stream processing capabilities, enabling you to build production-grade systems with confidence.

You will begin by developing a strong foundation in event streaming concepts and understanding how Kafka fits into modern distributed systems. You will explore the internal architecture of Kafka clusters, including brokers, topics, partitions, offsets, replication mechanisms, and the role of in-sync replicas in ensuring durability and high availability. The course also introduces essential components of the Confluent ecosystem, including the Schema Registry, helping you manage and evolve data schemas effectively in real-world scenarios.

As you progress, you will gain hands-on experience building Kafka producers and consumers, learning how messages are produced, partitioned, and delivered across distributed systems. You will master critical configuration settings, understand delivery guarantees such as at-most-once, at-least-once, and exactly-once semantics, and learn how to handle retries, failures, and edge cases that commonly occur in production environments. On the consumer side, you will explore consumer groups, partition rebalancing, offset management strategies, and efficient polling and threading models to ensure reliable and scalable data consumption.

The course goes beyond basic messaging by introducing Kafka Streams, a powerful library for building real-time stream processing applications. You will learn how to design and implement stream processing topologies, understand the differences between streams and tables, and apply both stateless and stateful operations. You will also explore advanced concepts such as state stores, time semantics, and windowing strategies, enabling you to process and analyze streaming data with precision and flexibility.

In addition to Kafka Streams, you will dive into ksqlDB, a SQL-based streaming platform that allows you to build stream processing applications using familiar query syntax. You will understand how to work with streams and tables in ksqlDB, differentiate between push and pull queries, and use it to simplify complex streaming use cases without writing extensive code.

Security is a critical aspect of any production system, and this course covers essential Confluent security concepts, including authentication, authorization, and role-based access control. You will learn how to secure your Kafka ecosystem and ensure that data access is properly managed across users and applications.

By the end of this course, you will have a complete understanding of Apache Kafka’s architecture and ecosystem, along with the practical skills needed to design, develop, and deploy real-time data streaming solutions.

Whether you are a developer, data engineer, or IT professional, this course equips you with the knowledge and confidence to work with Kafka in real-world environments and advance your career in modern data engineering.

Thank you

Who this course is for:

Developers who want to build real-time, event-driven applications using Apache Kafka
Data engineers looking to design and manage scalable data streaming pipelines
Backend engineers interested in distributed systems and high-throughput messaging
Software engineers who want to expand their skills into modern data architectures
Beginners who have basic programming knowledge and want to learn Apache Kafka from scratch
IT professionals and system architects aiming to understand and implement event-driven systems
Professionals preparing for roles in data engineering, streaming platforms, or big data technologies
Anyone interested in learning how modern companies process and analyze data in real time using Apache Kafka and the Confluent ecosystem

Confluent Certified Developer for Apache Kafka || 2026 ||

What you'll learn

Explore related topics

Course content

Apache Kafka Architecture and Fundamentals5 lectures • 22min

Developing Kafka Producers5 lectures • 21min

Developing Kafka Consumers5 lectures • 21min

Kafka Streams Architecture5 lectures • 21min

ksqlDB and Confluent Security Concepts5 lectures • 21min

Requirements

Description

Who this course is for: