Getting Started with Big Data Course Overview

Getting Started with Big Data Course Overview

The "Getting Started with Big Data" course is a comprehensive program designed to introduce learners to the expansive world of big data analytics. It aims to provide a foundation in understanding and utilizing big data tools and methodologies, specifically focusing on Hadoop and its ecosystem, as well as Apache Spark and Kafka.

Beginning with Module 1, participants will get a Big Data Overview that covers the essential Five Vs of Big Data and dives into the relationship between Big Data and Hadoop. The module further explores the Components of the Hadoop Ecosystem and introduces the basics of Big Data Analytics.

Module 2 shifts focus to HDFS (Hadoop Distributed File System) and Map Reduce, key components for big data storage and distributed processing. The lessons will clarify the Mapping and Reducing stages and familiarize learners with terms like Output Format, Partitioners, Combiners, and the Shuffle and Sort process.

PySpark Foundation is the core of Module 3, where learners will understand how to configure Spark and manipulate Resilient Distributed Datasets (RDDs), which are crucial for Aggregating Data in big data processing.

Module 4 contrasts Spark SQL with Hadoop Hive, guiding students through practical applications using the Spark SQL Query Language.

In Module 5, the course takes a leap into Machine Learning with Spark ML, covering various algorithms such as Linear Regression, Logistic Regression, and Random Forest.

Finally, Module 6 introduces the streaming platform Kafka, outlining its architecture, workflow, and cluster configuration.

Overall, this course will empower learners with the knowledge and practical skills needed to navigate the big data landscape, making them valuable assets in fields that require data-driven decision-making.

Purchase This Course

1,150

  • Live Online Training (Duration : 24 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • date-img
  • date-img

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings

images-1-1

1-on-1 Training

Schedule personalized sessions based upon your availability.

images-1-1

Customized Training

Tailor your learning experience. Dive deeper in topics of greater interest to you.

images-1-1

4-Hour Sessions

Optimize learning with Koenig's 4-hour sessions, balancing knowledge retention and time constraints.

images-1-1

Free Demo Class

Join our training with confidence. Attend a free demo class to experience our expert trainers and get all your queries answered.

Course Prerequisites

Certainly! Here are the minimum required prerequisites for successfully undertaking the "Getting Started with Big Data" course:


  • Basic understanding of programming principles and experience in a programming language such as Python, Java, or Scala.
  • Familiarity with fundamental concepts of databases and data structures.
  • Basic knowledge of Linux or Unix-based systems for navigating and simple commands, as Hadoop runs on these platforms.
  • Understanding of core statistical principles can be helpful, especially for the Machine Learning with Spark ML module.
  • While not mandatory, exposure to SQL and relational databases will be beneficial for grasping concepts in Spark SQL and Hadoop Hive modules.

These prerequisites are intended to ensure that learners can comfortably grasp the course material and fully benefit from the training. The course is designed with a step-by-step approach to accommodate learners who are new to Big Data, provided they come with the foundational knowledge listed above.


Target Audience for Getting Started with Big Data

"Become proficient in handling massive datasets with our Getting Started with Big Data course, tailored for IT professionals and data enthusiasts."


  • Data Analysts
  • Business Analysts
  • Data Scientists
  • IT Professionals interested in Big Data
  • Software Developers and Engineers
  • Data Engineers
  • Hadoop Developers
  • Machine Learning Engineers
  • Database Administrators
  • System Administrators aiming to manage Big Data tools
  • Graduates aspiring to build a career in Big Data Analytics
  • Technical Project Managers
  • Business Intelligence Professionals
  • Data Visualization Analyst
  • Research Professionals and Academicians in Data-Intensive disciplines
  • Technology Planners seeking integration of Big Data in business strategy


Learning Objectives - What you will Learn in this Getting Started with Big Data?

Course Introduction:

Gain a comprehensive understanding of Big Data concepts and tools through hands-on experience with Hadoop, MapReduce, PySpark, Spark SQL, machine learning with Spark ML, and real-time processing with Kafka.

Learning Objectives and Outcomes:

  • Understand the concept of Big Data and its significance in the modern data-driven landscape.
  • Identify the Five Vs of Big Data and how they impact data processing and analytics.
  • Gain foundational knowledge of Hadoop and its components within the Big Data ecosystem.
  • Learn the principles of distributed data storage using the Hadoop File System (HDFS).
  • Perform distributed data processing with MapReduce, understanding the mapping and reducing stages.
  • Develop practical skills in PySpark, including Spark configuration and operations on Resilient Distributed Datasets (RDDs).
  • Differentiate between Spark SQL and Hadoop Hive, and execute queries using Spark SQL.
  • Understand the basics of machine learning algorithms and implement them using Spark ML.
  • Grasp the architecture and workflow of Kafka for real-time data processing.
  • Execute a hands-on MapReduce task and work on aggregating data with pair RDDs in PySpark, reinforcing the theoretical knowledge with practical application.