Cloudera Data Engineering: Developing Applications with Apache Spark Course Overview

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.