Mastering Big Data with Hadoop Course Overview

To ensure that you have a productive and effective learning experience in the Mastering Big Data with Hadoop course, the following are the minimum required prerequisites:

• Basic understanding of Linux or Unix-based systems (as Hadoop runs on Linux).
• Familiarity with command-line interface operations, as they are frequently used in Hadoop.
• Fundamental knowledge of computer programming principles. Proficiency in a programming language such as Java is highly beneficial but not mandatory.
• An understanding of database concepts, including tables and simple SQL queries.
• Basic knowledge of data structures (e.g., arrays, lists, sets) and algorithms.
• A grasp of basic concepts in data processing, such as what constitutes big data and the challenges associated with it.
• Willingness to learn new software tools and technologies.

Prior experience with any specific big data tools is not required, as this course is designed to introduce you to the Hadoop ecosystem from the ground up.

Target Audience for Mastering Big Data with Hadoop

Mastering Big Data with Hadoop is designed for professionals seeking to leverage big data analytics for strategic insights.

• Data Analysts
• Data Scientists
• Business Intelligence Specialists
• Systems and Data Engineers
• IT Professionals with a focus on data processing
• Software Developers looking to specialize in Big Data solutions
• Technical Project Managers overseeing Big Data projects
• Database Professionals aiming to transition to Hadoop-based technologies
• Graduates aiming to build a career in Big Data Analytics
• Technical Architects and Consultants designing Big Data solutions
• Professionals in data-intensive industries like finance, retail, healthcare, utilities, and telecommunications

Learning Objectives - What you will Learn in this Mastering Big Data with Hadoop?

Introduction to Learning Outcomes:

Gain in-depth knowledge of Big Data and Hadoop ecosystem tools, including their architecture, core components, data processing, and analysis frameworks. Master Hadoop 2.x, YARN, MapReduce, Hive, Pig, HBase, Sqoop, Flume, and Spark.

Learning Objectives and Outcomes:

• Understand the concept of Big Data and the challenges associated with traditional data analytics architectures.
• Acquire knowledge of the Hadoop ecosystem and its components, including HDFS and MapReduce.
• Learn the architecture of YARN and its role in resource management and job scheduling.
• Set up single-node and multi-node Hadoop clusters and administer them effectively.
• Comprehend the MapReduce framework and develop an understanding of its operation and execution flow.
• Gain expertise in data scripting with Pig and managing and querying data with Hive.
• Understand the role of NoSQL databases in Big Data and learn the architecture and data model of HBase.
• Master data ingestion tools like Sqoop for importing data from RDBMS to Hadoop and Flume for streaming logs into Hadoop.
• Learn to utilize Spark for in-memory data processing to run programs faster than MapReduce.
• Apply the acquired skills in real-world scenarios and understand the practical aspects of Big Data processing.