Big Data: Hadoop| MapReduce| Hive| Pig| NoSQL| Mahout| Oozie (Udemy.com)

Welcome to our comprehensive course on Big Data and Hadoop! In this course, we dive deep into the world of big data technologies, focusing on Hadoop, one of the most powerful and widely used frameworks for processing large-scale data sets.

Throughout this course, you'll learn the fundamentals of Hadoop, including its architecture, components, and applications. We'll cover everything from the basics of big data and Hadoop to advanced topics such as MapReduce, HDFS, Hive, Pig, and more.

Whether you're a beginner looking to understand the basics of big data or an experienced professional seeking to enhance your skills in Hadoop ecosystem technologies, this course has something for everyone. Get ready to explore the exciting field of big data and unleash the power of Hadoop for solving real-world data challenges. Join us on this journey as we unlock the potential of big data together! We will learn the followings section-wise:

Section 1: Big Data and Hadoop Training Introduction

In this section, students are introduced to the foundational concepts of Big Data and Hadoop training. They begin by understanding the significance of Hadoop in handling large volumes of data efficiently. Through a series of introductory sessions, learners familiarize themselves with the landscape of Big Data and Hadoop technology, setting the stage for more in-depth exploration in subsequent sections.

Section 2: Hadoop Architecture and HDFS

Moving on to the architecture of Hadoop and its distributed file system (HDFS), this section delves into the core components of Hadoop 1.0. Students gain insights into the storage layer of Hadoop and the placement policies governing data distribution across the cluster. Through hands-on exercises and cluster setup tutorials, learners develop a solid understanding of Hadoop's architecture and its practical implementation in real-world scenarios.

Section 3: MapReduce Fundamentals

In this section, students dive into the fundamentals of MapReduce, a core component of Hadoop for processing and analyzing large datasets in parallel. Through a series of lectures, learners explore key concepts such as secondary sorting, composite keys, and the importance of partitioning. They gain hands-on experience with MapReduce programming by working on sample programs, understanding map-side joins, and implementing combiners for efficient data processing.

Section 4: MapReduce Advanced

Building upon the foundational knowledge of MapReduce, this section delves into more advanced topics and techniques for optimizing MapReduce programs. Students learn about running and debugging MapReduce programs, working with different file formats, and leveraging advanced MapReduce functionalities for tasks such as log processing and data export. By the end of this section, learners are equipped with the skills to tackle complex data processing challenges using MapReduce.

Section 5: HIVE Fundamentals

In this section, students are introduced to Apache Hive, a data warehouse infrastructure built on top of Hadoop for querying and analyzing large datasets stored in HDFS. Through a series of lectures, learners explore Hive's architecture, data modeling concepts, and query language (HiveQL). They learn how to create and manage databases and tables, perform data loading operations, and execute various SQL-like queries to extract insights from structured data.

Section 6: Hive Advanced

Expanding upon the foundational knowledge of Hive, this section covers advanced topics and techniques for optimizing Hive queries and data processing workflows. Students learn about partitioning, bucketing, indexing, and other performance optimization strategies to enhance query performance and scalability. Additionally, they explore advanced features such as table sampling, archiving, and working with slowly changing dimensions (SCD) to address complex data analysis requirements effectively.

Section 7: PIG Fundamentals

In this section, students explore Apache Pig, a high-level data flow scripting language for processing and analyzing large datasets in Hadoop. Through a series of lectures, learners discover Pig's features, data types, and operators for expressing data transformations and analysis tasks concisely. They gain hands-on experience with loading and storing data, grouping and joining operations, and leveraging built-in functions to perform data manipulation tasks efficiently.

Section 8: PIG Advanced

Building upon the foundational knowledge of Pig, this section delves into advanced topics and techniques for optimizing Pig scripts and data processing workflows. Students learn about debugging techniques, leveraging user-defined functions (UDFs), and working with complex data types to handle diverse data processing requirements effectively. Additionally, they explore strategies for improving Pig script performance and scalability in large-scale data processing environments.

Section 9: NoSQL Fundamentals

This section provides an introduction to NoSQL databases, covering their history, characteristics, and benefits in handling diverse and rapidly changing data types. Students learn about different types of NoSQL databases, including document-based, columnar, and graph databases, and understand their suitability for various use cases. Additionally, learners explore key concepts such as schema flexibility, consistency models, and distributed architecture, gaining insights into managing and querying data in NoSQL environments effectively.

Section 10: Apache Mahout

In this section, students explore Apache Mahout, a scalable machine learning library built on top of Hadoop for building and deploying machine learning models at scale. Through a series of lectures and hands-on exercises, learners discover Mahout's architecture, algorithms, and use cases in real-world scenarios. They gain practical experience in implementing recommendation systems, clustering, classification, and other machine learning tasks using Mahout's APIs and tools.

Section 11: Apache Oozie

This section introduces Apache Oozie, a workflow scheduler system for managing Hadoop jobs and data processing workflows. Students learn about Oozie's architecture, workflow definition language, and various workflow actions for coordinating and orchestrating complex data processing pipelines. Through hands-on exercises, learners gain proficiency in creating, scheduling, and monitoring workflows using Oozie, enabling them to automate and streamline data processing tasks effectively.

Section 12: Apache Flume