Apache Kafka Training in Radom

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level Java developers who wish to integrate Apache Kafka into their applications for reliable, scalable, and high-throughput messaging. By the end of this training, participants will be able to:

• Understand the architecture and core components of Kafka.
• Set up and configure a Kafka cluster.
• Produce and consume messages using Java.
• Implement Kafka Streams for real-time data processing.
• Ensure fault tolerance and scalability in Kafka applications.

This instructor-led, live training in Radom (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.
• Produce and consume messages using Kafka.
• Monitor and manage Kafka as a message queue.

This instructor-led, live training in Radom (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.
• Implement security measures for Kafka topics.

This instructor-led, live training in Radom (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.
• Integrate Kafka with Spring Boot.

This instructor-led, live training in Radom (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 instructor-led, live training in Radom (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 (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.

In this instructor-led, live training in Radom (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 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 Radom (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 Radom (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 Radom (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.

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 Radom (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 Radom (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.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Radom (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.

This instructor-led, live training in Radom (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.