Local, instructor-led live Apache Kafka training courses demonstrate through interactive discussion and hands-on practice how to set up and operate a Kafka message broker.
Kafka training is available as "onsite live training" or "remote live training". Onsite live Apache Kafka training can be carried out locally on customer premises in the US or in NobleProg corporate training centers in the US. Remote live training is carried out by way of an interactive, remote desktop.
Kafka training courses cover integration of Kafka with other Big Data systems as well as how to set up real-time data pipelines for streaming applications.
NobleProg -- Your Local Training Provider
Testimonials
★★★★★
★★★★★
What did you like the most about the training?:
Practical examples.
Jorge Creixell - Fyber GmbH
Course: Distributed Messaging with Apache Kafka
What did you like the most about the training?:
Interactivity.
Alan Daud - Fyber GmbH
Course: Distributed Messaging with Apache Kafka
A very professional good style which created an open and positive atmosphere among all participants.
Heiko Michel - Lufthansa Systems GmbH & Co KG
Course: Distributed Messaging with Apache Kafka
Informative and had correct level of detail I believe.
Asif Akhtar
Course: Distributed Messaging with Apache Kafka
I really was benefit from the easy to follow.
Zach Henke
Course: Distributed Messaging with Apache Kafka
The exercizes, and especially when they didn't work (obviously my fault but fault finding is part of the job).
Peter Hendriks
Course: Distributed Messaging with Apache Kafka
I found the programming example interesting.
Rostislaw Krassow
Course: Distributed Messaging with Apache Kafka
I mostly liked the knowledge of the Trainer.
Christian Langer
Course: Distributed Messaging with Apache Kafka
I genuinely liked the detail explanations, well prepared document.
Allen Jeong
Course: Distributed Messaging with Apache Kafka
I was benefit from the practical advice (for Kafka configuration and management).
OLAmobile
Course: Distributed Messaging with Apache Kafka
I was benefit from the practical examples, trainer new what he is talking about.
Rumos
Course: Distributed Messaging with Apache Kafka
The trainer really knows Kafka very well, and has a lot of production experience in the matter.
Matej Puntra
Course: Distributed Messaging with Apache Kafka
The training was steered in the direction what the team wanted. The trainer is too good with vast experience in handling concepts like capability, performance, development and deployment standards and very swift in the training in addressing queries from different levels like regarding code, design, architecture and best practices etc.
This instructor-led, live training in the US (onsite or remote) is aimed at developers who wish to integrate Apache Kafka with existing databases and applications for processing, analysis, etc.
By the end of this training, participants will be able to:
- Use Kafka Connect to ingest large amounts of data from a database into Kafka topics. - Ingest log data generated by an application servers into Kafka topics. - Make any collected data available for stream processing. - Export data from Kafka topics into secondary systems for storage and analysis.
This instructor-led, live training (onsite or remote) is aimed at engineers who wish to use Confluent (a distribution of Kafka) to build and manage a real-time data processing platform for their applications.
By the end of this training, participants will be able to:
- Install and configure Confluent Platform. - Use Confluent's management tools and services to run Kafka more easily. - Store and process incoming stream data. - Optimize and manage Kafka clusters. - Secure data streams.
Format of the Course
- Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment.
Course Customization Options
- This course is based on the open source version of Confluent: Confluent Open Source. - To request a customized training for this course, please contact us to arrange.
Apache Kafka is a distributed streaming platform. It is de facto a standard for building data pipelines and it solves a lot of different use-cases around data processing: it can be used as a message queue, distributed log, stream processor, etc.
We'll start with some theory behind data pipelines in general, then continue with fundamental concepts behind Kafka. We'll also discover important components like Kafka Streams and Kafka Connect.
Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously.
In this instructor-led, live training (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming. - Understand and select the most appropriate framework for the job. - Process of data continuously, concurrently, and in a record-by-record fashion. - Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc. - Integrate the most appropriate stream processing library with enterprise applications and microservices.
Audience
- Developers - Software architects
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
This course is for enterprise architects, developers, system administrators and anyone who wants to understand and use a high-throughput distributed messaging system. If you have more specific requirements (e.g. only system administration side), this course can be tailored to better suit your needs.
This instructor-led, live training in the US (onsite or remote) is aimed at sysadmins who wish to set up, deploy, manage and optimize an enterprise-grade Kafka cluster.
By the end of this training, participants will be able to:
- Set up and administer a Kafka Cluster. - Evaluate the benefits and disadvantages of deploying Kafka on-premise vs in the cloud. - Deploy and monitor Kafka in using various on-premise and cloud environment tools.
This instructor-led, live training in the US (onsite or remote) is aimed at software testers who wish to implement network security measures into an Apache Kafka application.
By the end of this training, participants will be able to:
- Deploy Apache Kafka onto a cloud based server. - Implement SSL encryption to prevent attacks. - Add ACL authentication to track and control user access. - Ensure credible clients have access to Kafka clusters with SSL and SASL authentication.
Kafka Streams is a client-side library for building applications and microservices whose data is passed to and from a Kafka messaging system. Traditionally, Apache Kafka has relied on Apache Spark or Apache Storm to process data between message producers and consumers. By calling the Kafka Streams API from within an application, data can be processed directly within Kafka, bypassing the need for sending the data to a separate cluster for processing.
In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing.
By the end of this training, participants will be able to:
- Understand Kafka Streams features and advantages over other stream processing frameworks - Process stream data directly within a Kafka cluster - Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams - Write concise code that transforms input Kafka topics into output Kafka topics - Build, package and deploy the application
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange
This instructor-led, live training in the US (onsite or remote) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.
By the end of this training, participants will be able to:
- Install and configure Confluent KSQL. - Set up a stream processing pipeline using only SQL commands (no Java or Python coding). - Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL. - Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management.
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages. - Decouple the handling of messages from an application. - Use Samza to implement near-realtime asynchronous computation. - Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training in the US (onsite or remote) is aimed at developers who wish to transform traditional architecture into a highly concurrent microservices-based architecture using Spring Cloud, Kafka, Docker, Kubernetes and Redis.
By the end of this training, participants will be able to:
- Set up the necessary development environment for building microservices. - Design and implement a highly concurrent microservices ecosystem using Spring Cloud, Kafka, Redis, Docker and Kubernetes. - Transform monolithic and SOA services to microservice based architecture. - Adopt a DevOps approach to developing, testing and releasing software. - Ensure high concurrency among microservices in production. - Monitor microservices and implement recovery strategies. - Carry out performance tuning. - Learn about future trends in microservices architecture.
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