Hadoop Developer with Spark Course Overview

Hadoop Developer with Spark Course Overview

The Hadoop Developer with Spark course is designed to equip learners with the skills needed to build big data processing applications using Apache Hadoop and Apache Spark. It is an excellent pathway for those preparing for the CCA 175 certification, as it covers the necessary topics and provides hands-on experience. Throughout the course, participants will explore the Hadoop ecosystem, understand HDFS architecture, and work with YARN for resource management.

The course delves into the basics of Apache Spark, DataFrame operations, and Spark SQL for querying data, which are crucial for the CCA 175 certification. Learners will also gain practical knowledge of RDDs, data persistence, and Spark streaming, all of which are part of the CCA 175 exam syllabus. By the end of the course, participants will be proficient in writing, configuring, and running Spark applications, setting them on the path to becoming certified Hadoop professionals with a focus on Spark.

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  • Live Online Training (Duration : 40 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
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♱ Excluding VAT/GST

Classroom Training price is on request

  • Live Online Training (Duration : 40 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

  • Power Packed 19 Hours (Edited from 40 hours of Live Training)
  • 6 Months Access to Videos
  • Access via Laptop, Tab, Mobile, and Smart TV
  • Certificate of Completion
  • Hands-on labs
  • 110+ Tests Questions (Qubits)




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Course Prerequisites

Certainly! Here are the minimum required prerequisites for the Hadoop Developer with Spark course presented in bullet point format:

  • Basic understanding of programming principles and data structures.
  • Familiarity with any high-level programming language, preferably Java, Scala, or Python, as Spark examples may be given in these languages.
  • Basic knowledge of Linux or Unix-based systems for navigating through the command line.
  • Fundamental understanding of database concepts and query language (SQL).
  • An introductory comprehension of big data and distributed systems.
  • Willingness to learn new technologies and adapt to the Hadoop ecosystem.

Please note that while prior experience with Hadoop or Spark is beneficial, it is not mandatory. This course is designed to introduce participants to Apache Hadoop and Spark, and it will cover the necessary components and tools throughout the training modules.

Target Audience for Hadoop Developer with Spark

Learn big data processing with Hadoop and Spark - a course for IT professionals aiming to master scalable data solutions.

  • Data Engineers
  • Software Developers with a focus on big data
  • Big Data Analysts
  • System Administrators interested in big data infrastructure
  • IT professionals looking to specialize in data processing
  • Data Scientists who want to add big data processing skills
  • Technical Leads managing big data projects
  • Database Professionals transitioning to big data roles
  • Graduates aiming to build a career in big data
  • IT Architects designing big data solutions systems

Learning Objectives - What you will Learn in this Hadoop Developer with Spark?

Introduction to Learning Outcomes

The Hadoop Developer with Spark course equips participants with comprehensive knowledge of data processing in the Hadoop ecosystem, including mastery of Apache Spark for real-time analytics.

Learning Objectives and Outcomes

  • Understand the fundamental concepts of Apache Hadoop and its role in the big data ecosystem.
  • Gain proficiency in HDFS architecture, data ingestion, storage operations, and cluster components.
  • Learn distributed data processing using YARN and develop the capability to work with YARN applications.
  • Acquire hands-on experience with Apache Spark, including Spark Shell, Datasets, DataFrames, RDDs, and Spark SQL.
  • Master data transformation, querying, and aggregation techniques using Spark's core abstractions and APIs.
  • Develop and configure robust Spark applications, understanding deployment modes and application tuning.
  • Grasp the concept of distributed processing, including partitioning strategies and job execution planning.
  • Learn data persistence methods and storage levels within Spark for optimized data handling.
  • Explore common data processing patterns, including iterative algorithms and machine learning with Spark's MLlib.
  • Dive into real-time data processing with Apache Spark Streaming, understanding DStreams, window operations, and integrating with sources like Apache Kafka.


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