What is the price of the Training Kafka - Master Real-Time Data Streams training?

The price of the Training Kafka - Master Real-Time Data Streams training is 5250 € HT. This includes access to all modules, training materials and personalized support. Funding solutions are available through OPCO.

Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

What is the duration of the Training Kafka - Master Real-Time Data Streams training?

The Training Kafka - Master Real-Time Data Streams training takes place over 35 hours. The program is structured in progressive modules for optimal skill development.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Kafka - Master Real-Time Data Streams training are: Knowledge of Java or Python, basics in distributed systems, experience with NoSQL databases.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Kafka - Master Real-Time Data Streams training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Kafka - Master Real-Time Data Streams | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Kafka - Master Real-Time Data Streams training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Kafka - Master Real-Time Data Streams training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Kafka - Master Real-Time Data Streams training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training access terms and deadlines

Training objectives

Set up a scalable and resilient Kafka cluster

Develop high-performance producers and consumers

Implement Kafka Streams for real-time processing

Secure streams with SSL and ACL

Optimize performance and monitor in production

Integrate Kafka with Spark and Elasticsearch

Deploy complete pipelines on real-world cases

Training program

Module 1Kafka Installation: Distributed Cluster (Docker, Zookeeper, Kafka Broker)

Install a multi-node Kafka cluster with Docker, configure Zookeeper for coordination, create your first topics and partitions, test high availability through practical exercises on failure scenarios, produce and consume your first messages to validate the solid basics of a reliable stream.

Module 2Advanced Producers and Consumers: Serialization, Offsets (Kafka Java API)

Develop asynchronous producers with compression and retries, implement consumers in group mode with automatic rebalancing, manage offsets manually through exercises on high-volume streams, integrate Avro for schema-based serialization, and produce a deliverable: a functional producer-consumer pipeline.

Module 3Kafka Streams: Stream Processing (Streams API, KTables, Joins)

Master Kafka Streams to transform real-time streams, code topologies with aggregations and time windows, integrate KTables for stateful joins, apply to concrete cases like IoT log analysis, test resilience with exactly-once semantics, and deploy your first scalable Streams app.

Module 4Kafka Security and Monitoring: SSL, ACL, Prometheus (Kafka Manager)

Configure security with SSL/TLS and Kerberos, implement ACLs to control topic access, monitor metrics via Prometheus and Grafana, diagnose bottlenecks on simulated clusters, resolve real incidents through practical exercises, and secure a complete cluster as a final deliverable.

Module 5Kafka Integrations and Production: Spark, Elasticsearch (Helm Kubernetes)

Integrate Kafka with Spark Streaming and Elasticsearch for end-to-end pipelines, deploy on Kubernetes via Helm, optimize for horizontal scaling, simulate massive loads with 1 million messages/second, build a capstone project on an e-commerce use case, and prepare for production deployment with best practices.