A Practical Introduction to Stream Processing Training Course

This instructor-led, live training in Uzbekistan (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.

Learn to implement end-to-end big data streaming use cases. Real-time data preparation and maintenance with Informatica, Edge, Kafka and Spark. This training covers software versions 10.2.1 and up.

This instructor-led, live training (online or onsite) is designed for engineers who want to utilize 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 the Confluent Platform.
• Use Confluent's management tools and services to streamline Kafka operations.
• Store and process incoming stream data efficiently.
• Optimize and manage Kafka clusters effectively.
• Secure data streams with robust measures.

Course Format

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options for the Course

• 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 that has become the de facto standard for building data pipelines. It addresses a wide range of data processing use cases, functioning as a message queue, distributed log, stream processor, and more.

We will begin by exploring the theoretical foundations of data pipelines in general, then delve into the core concepts underlying Kafka. Additionally, we will examine key components such as Kafka Streams and Kafka Connect.

This course is designed for enterprise architects, developers, system administrators, and anyone interested in understanding and utilizing a high-throughput distributed messaging system. Should you have more specific requirements, such as focusing solely on the system administration aspect, the course can be customized to better meet your needs.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level system administrators and operations engineers who wish to use Apache Kafka to deploy, secure, monitor, and troubleshoot Kafka clusters.

By the end of this training, participants will be able to: explain Kafka architecture and KRaft mode, operate and secure Kafka clusters, monitor performance and reliability, and resolve common production issues.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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 Uzbekistan (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 designed for building applications and microservices that process data within a Kafka messaging system. Traditionally, Apache Kafka has relied on external tools like Apache Spark or Apache Storm to handle data processing between producers and consumers. However, by integrating the Kafka Streams API into an application, data can be processed directly within Kafka, eliminating the need to send it 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 exchange data with Apache Kafka for stream processing.

By the end of this training, participants will be able to:

• Understand the features and advantages of Kafka Streams over other stream processing frameworks
• Process stream data directly within a Kafka cluster
• Develop a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
• Write efficient 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 extensive hands-on practice

Notes

• To request a customized training for this course, please contact us to arrange

Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.

Target Audience: Intermediate-level professionals in the banking industry familiar with Python and Spark, seeking to deepen their skills in big data processing and machine learning.

This instructor-led, live training in Uzbekistan (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.

In this instructor-led, live training in Uzbekistan, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Uzbekistan (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.

Stratio is a data-centric platform that integrates big data, AI, and governance into a single solution. The Rocket and Intelligence modules of Stratio facilitate rapid data exploration, transformation, and advanced analytics in enterprise settings.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level data professionals who wish to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

By the end of this training, participants will be able to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Use loops and conditional logic to manage data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

• Interactive lecture and discussion sessions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.