Online or onsite, 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 "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Apache Kafka trainings in Zapopan can be carried out locally on customer premises or in NobleProg corporate training centers.
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.
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Situated in the most exclusive area in Guadalajara this business centre is conveniently located with easy access to Andares S...
Situated in the most exclusive area in Guadalajara this business centre is conveniently located with easy access to Andares Shopping Center, the Mexico Plaza Hotel, restaurants and entertainment.
This instructor-led, live training in Zapopan (online or onsite) is aimed at intermediate-level system administrators who wish to harness Kafka's message queuing features effectively.By the end of this training, participants will be able to:
Understand Kafka's message queuing capabilities and architecture.
Configure Kafka topics for message queuing scenarios.
This instructor-led, live training in Zapopan (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to learn how to effectively manage Kafka topics for efficient data streaming and processing.By the end of this training, participants will be able to:
Understand Kafka topic fundamentals and architecture.
Create, configure, and manage Kafka topics.
Monitor Kafka topics for health, performance, and availability.
This instructor-led, live training in Zapopan (online or onsite) is aimed at intermediate-level developers who wish to learn the fundamentals of Kafka and integrate it with Spring Boot.
By the end of this training, participants will be able to:
Understand Kafka and its architecture.
Learn how to install, configure, and set up a basic Kafka environment.
This instructor-led, live training in Zapopan (online or onsite) is aimed at intermediate-level developers who wish to develop big data applications with Apache Kafka.
By the end of this training, participants will be able to:
Develop Kafka producers and consumers to send and read data from Kafka.
Integrate Kafka with external systems using Kafka Connect.
Write streaming applications with Kafka Streams & ksqlDB.
Integrate a Kafka client application with Confluent Cloud for cloud-based Kafka deployments.
Gain practical experience through hands-on exercises and real-world use cases.
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.
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
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
In this instructor-led, live training in Zapopan (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.
This instructor-led, live training (online or onsite) 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.
This instructor-led, live training in Zapopan (online or onsite) 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.
This instructor-led, live training in Zapopan (online or onsite) 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 in Zapopan (online or onsite) 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.
This instructor-led, live training in Zapopan (online or onsite) 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 Zapopan (online or onsite) 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.
This instructor-led, live training in Zapopan (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.
By the end of this training, participants will be able to:
Implement a data pipeline architecture for processing big data.
Develop a cluster infrastructure with Apache Mesos and Docker.
This instructor-led, live training in Zapopan (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python.
By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.
This instructor-led, live training in Zapopan (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.
By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.
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Testimonials (7)
Francis' way of explaining is clear, he presents examples that help with the explanation
JUAN JOSÉ LÓPEZ MARTÍNEZ - Bisih
Course - Apache Kafka for Developers
Machine Translated
Could throw questions at him and he would jump to slides or example to try and show and answer all our questions
Johannes - Canon Medical Informatics Europe B.V.
Course - Administration of Kafka Topic
To the point, proper pace (bash basics required though)
Krzysztof - Agora SA
Course - Kafka for Administrators
Las explicaciones eran muy buenas, si bien algunas preguntas pudieron ahorrarse si se hubieran tocado esos puntos al inicio de los temas se notó un buen dominio y experiencia en el tema.
Alan Jaime Rodríguez García - BANCO DE MEXICO
Course - Stream Processing with Kafka Streams
very interactive...
Richard Langford
Course - SMACK Stack for Data Science
Sufficient hands on, trainer is knowledgable
Chris Tan
Course - A Practical Introduction to Stream Processing
I liked his pace for training, it was optimum.
Edwards Mukasa - AFRINIC Ltd.
Course - Microservices with Spring Cloud and Kafka
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