Apache Kafka - Streaming Platform Mastery
Become a production-ready Kafka engineer: producers and consumers, Kafka Streams, Connect, Schema Registry, cluster operations, security, and the observability that keeps real-time data flowing.
Kafka fundamentals
The program starts with what Kafka really is: the distributed log and topics, producers and their delivery guarantees, consumers and consumer groups, partitions, offsets, and ordering, and serialization with the Schema Registry. Understanding the log abstraction is the key to everything Kafka does, and getting the delivery guarantees right is what makes a streaming system trustworthy.
These fundamentals matter because Kafka's power comes from a few deep ideas applied consistently. Once you understand the log, partitioning, and consumer groups, the rest of the platform, streams, connect, and operations, follows naturally, which is why the program grounds you thoroughly here first.
Getting the delivery guarantees right is what makes a streaming system trustworthy, which is why the fundamentals get such careful treatment.
Stream processing and integration
The program covers processing streams with Kafka Streams, stateful processing and stores, windowing and joins, ksqlDB for streaming SQL, and exactly-once processing. It then covers integration through Kafka Connect: source and sink connectors, change data capture into Kafka, and connecting to warehouses and lakes.
This combination, processing plus integration, is what turns Kafka from a message bus into a real-time data platform. Building an event-driven pipeline that captures changes, processes them in flight, and lands them in analytical systems is exactly the architecture modern data platforms are moving toward.
Combining processing with integration is what turns Kafka from a message bus into a real-time data platform.
Operations and reliability
Kafka is infrastructure, so operating it well is essential, and the program covers cluster design with replication and partitioning, high availability and disaster recovery, security and access control, and monitoring with Prometheus and Grafana. This operational depth is what distinguishes an engineer who can use Kafka from one who can run it in production.
The streaming capstone brings it together: a change-data-capture pipeline through Connect, a Kafka Streams application with exactly-once semantics, schema-governed topics, and a highly available, monitored cluster. Running Kafka reliably at scale is exactly the kind of platform ownership that leads toward senior and principal roles.
Running Kafka reliably at scale is a distinctive form of platform ownership that leads directly toward senior and principal roles.
See the method, not just the topic
A representative worked example from the program, so you can see the level of concreteness the curriculum works at.
// A producer configured so retries never create duplicates and
// a batch either fully commits or not at all. Correct delivery
// guarantees are the foundation of a trustworthy streaming system.
Properties props = new Properties();
props.put("bootstrap.servers", "broker1:9092,broker2:9092");
props.put("enable.idempotence", "true"); // no duplicate writes
props.put("acks", "all"); // wait for replicas
props.put("transactional.id", "sales-producer-1"); // enables txns
KafkaProducer<String, String> producer = new KafkaProducer<>(props);
producer.initTransactions();
try {
producer.beginTransaction();
producer.send(new ProducerRecord<>("sales", key, value));
producer.commitTransaction(); // atomic
} catch (KafkaException e) {
producer.abortTransaction(); // all or nothing
}
// idempotence + acks=all + transactions give exactly-once
// semantics end to end, the guarantee real pipelines depend on.The full syllabus
Four modules of five chapters each, sequenced so the material builds cumulatively. Each chapter carries a note on what it teaches.
Module 1Kafka fundamentals
- 01The Kafka log and topicsThe Kafka log and topics. The log is the idea everything rests on.
- 02Producers and delivery guaranteesProducers and delivery guarantees. Delivery guarantees decide correctness.
- 03Consumers and consumer groupsConsumers and consumer groups. Consumer groups scale out consumption.
- 04Partitions, offsets, and orderingPartitions, offsets, and ordering. Partitions give ordering and parallelism.
- 05Serialization and the Schema RegistrySerialization and the Schema Registry. The Schema Registry keeps data compatible.
Module 2Stream processing
- 06Kafka StreamsProcessing with Kafka Streams. Kafka Streams processes data in flight.
- 07Stateful processing and storesStateful processing and stores. State stores let streams remember.
- 08Windowing and joinsWindowing and joins. Windows and joins enrich streaming data.
- 09ksqlDB for streaming SQLStreaming SQL with ksqlDB. ksqlDB expresses streaming logic in SQL.
- 10Exactly-once processingExactly-once processing. Exactly-once keeps results correct end to end.
Module 3Integration
- 11Kafka ConnectIntegration with Kafka Connect. Connect integrates Kafka without custom code.
- 12Source and sink connectorsSource and sink connectors. Connectors link Kafka to other systems.
- 13Change data capture into KafkaChange data capture into Kafka. CDC streams database changes into Kafka.
- 14Integrating with warehouses and lakesConnecting to warehouses and lakes. Kafka feeds the warehouses and lakes.
- 15Building an event-driven pipelineBuilding an event-driven pipeline. You assemble a full event-driven pipeline.
Module 4Operations and reliability
- 16Cluster design: replication and partitioningCluster design: replication and partitioning. Cluster design determines reliability.
- 17High availability and disaster recoveryHigh availability and disaster recovery. HA and DR keep streams flowing through failures.
- 18Security and access controlSecurity and access control. Security protects the data in motion.
- 19Monitoring with Prometheus and GrafanaMonitoring with Prometheus and Grafana. Monitoring is essential for infrastructure.
- 20The Kafka streaming capstoneBuilding the Kafka streaming capstone. The capstone is a running streaming platform.
How the program is taught
The program is hands-on and project-driven: you work with Apache Kafka from fundamentals to operating it in production through real labs rather than watching from a distance, and it builds toward a capstone you can keep and show. Every concept is applied, because platform skills are built by doing, not by reading about them.
It is structured so a motivated learner can start where they are and build steadily, with worked examples and code throughout. The through-line is always real, production-shaped work, so at every stage you are learning the platform the way practitioners actually use it.
Prerequisites and pace
Programming helps for the Streams material, and the data-engineering foundation gives context. The program builds Kafka from its fundamentals. The pace builds from foundations to a capstone, and the most effective approach is to complete each lab rather than skim it, since the labs accumulate into the project.
For someone working toward the senior end of this track, consistency matters more than speed: steady progress through the material, and through the capstone, is what builds durable capability and a portfolio that demonstrates it.
What makes this program different
It covers both development and operations, so you can not only build with Kafka but run reliable, secure, monitored clusters, which is what platform ownership means. That focus is what turns knowledge of a platform into the ability to build and operate real systems on it.
The other distinction is the orientation toward the whole journey. Every program in this track is designed to fit with the others and to build toward the senior technical-leadership destination, so this one is taught as a step on that path rather than an isolated course.
What you will be able to do
- Build reliable producers and consumers with the right guarantees
- Process streams with Kafka Streams and ksqlDB
- Integrate systems with Kafka Connect and change data capture
- Design highly available, secure Kafka clusters
- Operate and monitor Kafka in production
Who should take it
- Data engineers building streaming systems
- Backend engineers integrating Kafka
- Platform engineers operating Kafka clusters
- Architects designing event-driven systems
Common questions
Do I need Spark before Kafka? No; they are complementary. Spark and Kafka often work together, and either can be learned first. This program is self-contained.
Is this developer or operations focused? Both, deliberately. Real Kafka value comes from being able to build streaming applications and operate the cluster they run on.
Where it leads, toward Principal
In the near term, this program opens Streaming and Platform Engineer roles, and toward senior and principal roles that own real-time infrastructure. The capstone is concrete evidence of that capability, which matters more than any list of topics studied.
In the longer term, it is one rung on the ladder toward Principal Engineer and Director of Cloud and Data Platforms. That destination is reached through breadth across the whole stack plus the architecture, operations, and leadership judgment the senior programs emphasize, and this program contributes a genuine, in-demand piece of that breadth.
How it fits the journey
This program is the second processing-and-streaming program. It rests on the foundations before it and connects to the programs around it, so taking it in sequence builds cumulative command rather than isolated knowledge.
Because every program is also complete in itself, you can enter here if this is exactly the platform your goals require, and still get a whole, finished program. The sequence is a guide, not a gate, all the way up to the Principal and Director destination.
What you build and keep
Build and operate an event-driven streaming platform on Kafka: change-data-capture ingestion through Connect, a Kafka Streams processing application with exactly-once semantics, a schema-governed topic design, and a highly available cluster with monitoring in Prometheus and Grafana.
Format: Self-paced with hands-on labs across development and operations, to a streaming capstone.
Run this program for your team
Every program can be delivered as a private, tailored cohort for your organization, aligned to your systems, policies, and career frameworks.
Scope a corporate cohortFrequently asked questions
What is the Apache Kafka - Streaming Platform Mastery program?
Become a production-ready Kafka engineer: producers and consumers, Kafka Streams, Connect, Schema Registry, cluster operations, security, and the observability that keeps real-time data flowing.
Who is this program for?
It suits data engineers building streaming systems, along with others described on this page.
How is it delivered?
Self-paced with hands-on labs across development and operations, to a streaming capstone.
Is there a project or capstone?
Build and operate an event-driven streaming platform on Kafka: change-data-capture ingestion through Connect, a Kafka Streams processing application with exactly-once semantics, a schema-governed topic design, and a highly available cluster with monitoring in Prometheus and Grafana.
How does this fit the wider journey?
Kafka completes the processing-and-streaming stage and integrates with Spark, the warehouses, and the databases. Owning real-time infrastructure is a distinctive senior skill, and event-driven architecture is core to the platforms principal engineers design.
Can my organization run this as a private cohort?
Yes. Every program can be delivered as a tailored corporate cohort. Contact us to scope it.