Cloudera Data Engineering: Developing Applications with Apache Spark Course Overview

Cloudera Data Engineering: Developing Applications with Apache Spark Course Overview

The Cloudera Data Engineering: Developing Applications with Apache Spark course is a comprehensive training program designed for developers and data engineers to master the intricacies of Spark application development. It covers the entire ecosystem surrounding Spark, including HDFS, YARN, and data processing frameworks. Starting with an introduction to Zeppelin notebooks, the course progresses through fundamental Hadoop components and moves into the evolution of distributed processing.

Learners will gain hands-on experience with RDDs, DataFrames, and Hive integration, as well as data visualization techniques. They will also tackle challenges in distributed processing and learn how to write, configure, and run Spark applications effectively. The course delves into structured streaming and real-time processing with Apache Kafka, teaching participants how to aggregate and join streaming DataFrames. Finally, an appendix is provided for those interested in working with Datasets in Scala.

By the end of this course, learners will have a solid foundation in Spark and its associated technologies, enabling them to build scalable and efficient data engineering solutions.

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Course Prerequisites

To ensure that you have a productive and enlightening experience in the Cloudera Data Engineering: Developing Applications with Apache Spark course, the following are the minimum required prerequisites:

  • Basic Understanding of Big Data Concepts: Familiarity with the concept of big data and its challenges would be beneficial.

  • Programming Knowledge: Some experience in programming, preferably in Scala or Python, as Apache Spark applications are commonly written in these languages.

  • Fundamentals of SQL: Knowledge of SQL and database concepts, since Spark interfaces with data through similar query mechanisms.

  • Linux Basics: Basic command-line skills in a Linux environment for navigating HDFS and executing Spark jobs.

  • Conceptual Knowledge of Distributed Systems: Understanding the basics of distributed computing will help in grasping the distributed nature of Hadoop and Spark processing.

  • Familiarity with Data Processing: Some experience with data processing tasks, which could include database management, data analysis, or ETL operations.

Note: While these prerequisites are recommended, the course is designed to accommodate a range of skill levels, and instructors will guide you through the foundational concepts necessary for mastering Apache Spark.

Target Audience for Cloudera Data Engineering: Developing Applications with Apache Spark

  1. The Cloudera Data Engineering course is designed for professionals seeking expertise in Apache Spark and big data ecosystems.

  2. Target Job Roles and Audience:

  • Data Engineers
  • Software Developers with a focus on big data processing
  • Apache Spark Developers
  • Big Data Architects
  • IT Professionals transitioning into big data roles
  • Data Scientists interested in engineering large-scale data processes
  • System Administrators managing Hadoop and Spark environments
  • Database Administrators expanding skills to include HDFS and big data platforms
  • DevOps Engineers involved in deployment and management of big data applications
  • Technical Project Managers overseeing big data projects
  • Graduates and Academics pursuing a career in big data technologies
  • Technical Consultants providing solutions for distributed computing challenges

Learning Objectives - What you will Learn in this Cloudera Data Engineering: Developing Applications with Apache Spark?

Course Learning Outcomes and Concepts Overview

This course equips students with hands-on experience in developing applications using Apache Spark, focusing on core competencies of data processing, analysis, and persistence in distributed systems.

Learning Objectives and Outcomes

  • Gain familiarity with interactive data exploration using Apache Zeppelin notebooks.
  • Understand HDFS architecture, components, and how to perform basic file operations on HDFS.
  • Learn the fundamentals of YARN and how it manages resources in a cluster.
  • Explore the evolution of distributed processing and the role of disk, memory, and GPUs.
  • Develop proficiency in working with Resilient Distributed Datasets (RDDs) and DataFrames for scalable data processing.
  • Integrate Spark with Hive for enhanced data querying and analysis capabilities.
  • Master data visualization techniques using Zeppelin for insightful analytics and collaboration.
  • Address distributed processing challenges such as shuffle operations, data skew, and ordering.
  • Write, configure, and run Spark applications effectively, understanding different deployment modes and the Spark Application Web UI.
  • Implement structured streaming applications and understand how to process streaming data in real-time.
  • Work with Apache Kafka for message processing, learning to scale and manage Kafka clusters.
  • Perform aggregations and joins on streaming DataFrames, leveraging Spark's structured streaming.
  • For Scala users, learn how to work with Datasets in Scala for type-safe data processing.