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Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP Course Overview

Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP Course Overview

The "Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP" course is designed to equip learners with the practical skills and theoretical knowledge needed to implement real-time streaming analytics using Apache Flink within the Cloudera Data Platform (CDP). The course begins with an introduction to Apache Flink and stream processing, outlining typical use cases and related products. It then guides students through service deployment, including planning, installation, and utilizing the Flink Dashboard.

As learners progress, they delve into the foundations of Flink, understanding its architecture, and mastering data stream processing with the DataStream API. The course emphasizes the development of stateful stream processing applications, the role of event time processing, and the use of watermarks and windows. Additionally, it introduces the Cloudera SQL Stream Builder, making it easy to build streaming SQL applications.

In later modules, students learn about the Flink SQL and Table API, integrations, and the importance of monitoring metrics and managing resource configurations. Through practical exercises, participants gain hands-on experience building, deploying, and monitoring streaming applications. This course prepares learners to harness the power of streaming data, a crucial skill for data engineers and analysts in today's fast-paced data-driven world.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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  • Live Online Training (Duration : 16 Hours)
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Course Prerequisites

To ensure that you can fully benefit from the Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP course, the following are the minimum required prerequisites:


  • Basic understanding of big data concepts and distributed computing principles.
  • Familiarity with command-line interface (CLI) operations and navigating a Linux environment.
  • Fundamental knowledge of SQL, including the ability to write and understand SQL queries.
  • Experience with a programming language, preferably Java or Scala, as Apache Flink applications are often written in these languages.
  • An introductory level of knowledge about data processing frameworks such as Apache Kafka for message queuing or real-time data pipelines.
  • Awareness of the concepts behind data streaming and stream processing.

While these prerequisites are aimed at setting a foundation for the course, learners with a strong motivation to learn and a willingness to engage with new concepts will find the course accessible. It is designed to bring participants up to speed with the necessary skills and knowledge to effectively use Apache Flink and SQL Stream Builder on the Cloudera Data Platform (CDP).


Target Audience for Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP

  1. This course covers Apache Flink and SQL Stream Builder for real-time data processing and analytics on the CDP platform.


    • Data Engineers
  • Data Scientists
  • Software Developers
  • DevOps Engineers
  • IT Architects
  • System Administrators
  • Data Analytics Managers
  • Technical Leads
  • Streaming Application Developers
  • Big Data Analysts
  • Cloud Solutions Architects
  • Database Administrators
  • IT Professionals seeking to leverage Apache Flink for stream processing


Learning Objectives - What you will Learn in this Cloudera Streaming Analytics: Using Apache Flink and SQL Stream Builder on CDP?

Introduction to Learning Outcomes:

Gain expertise in Cloudera Streaming Analytics using Apache Flink and SQL Stream Builder, mastering real-time data processing, stream processing applications, and leveraging Kafka integrations within Cloudera Data Platform (CDP).

Learning Objectives and Outcomes:

  • Understand the fundamentals of Apache Flink and its role in stream processing.
  • Identify typical use cases for stream processing and how Flink addresses them.
  • Learn to deploy Flink services, including installation and utilizing the Flink Dashboard.
  • Develop the skills to create, build, and run Flink applications effectively.
  • Comprehend the architecture of Flink, including logical and physical layouts, parallelism, fault tolerance, and data storage.
  • Utilize the DataStream API for creating real-time streaming applications and batch processing.
  • Integrate Kafka as a source and sink in Flink applications.
  • Explore Flink SQL and Table API for stream processing with options for integration.
  • Implement stateful stream processing, managing checkpoints, event time processing, and windowing functions.
  • Leverage Cloudera SQL Stream Builder for building and executing streaming SQL jobs and monitor applications for performance tuning and optimization.