Master Apache Hadoop - Infinite Skills Hadoop Training from Udemy

This Introduction to Apache Hadoop training course from Infinite Skills will teach you the tools and functions needed to work within this open-source software framework. This course is designed for the absolute beginner, meaning no prior experience with Hadoop is required.

You will start out by learning the basics of Hadoop, including the Hadoop run modes and job types and Hadoop in the cloud. You will then learn about the Hadoop distributed file system (HDFS), such as the HDFS architecture, secondary name node, and access controls. This video tutorial will also cover topics including MapReduce, debugging basics, hive and pig basics, and impala fundamentals. Finally, this course will teach you how to import and export data. Once you have completed this computer based training video, you will be fully capable of using the tools and functions you’ve learned to work successfully in Hadoop. Working files are included, allowing you to follow along with the author throughout the lessons.

What's inside

Syllabus

Introduction

Important - Download These First - Working Files

0101 What Is Big Data?

0103 Historical Approaches

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides a comprehensive introduction to Hadoop, covering essential components like HDFS, MapReduce, Hive, Pig, and Impala, which are foundational for big data processing

Includes hands-on labs using Cloudera, a popular Hadoop distribution, allowing learners to gain practical experience with industry-standard tools and workflows

Explores data import and export options, including Flume and Sqoop, which are crucial for integrating Hadoop with other data sources and systems

Covers debugging techniques and benchmarking tools like Teragen and Terasort, which are valuable for optimizing Hadoop performance and troubleshooting issues

Focuses on an older distribution of Hadoop, which may not reflect the latest features and best practices found in more recent versions

Reviews summary

Solid introduction with setup hurdles

According to learners, this course serves as a solid introduction to core Hadoop concepts like HDFS and MapReduce. Many find the explanations clear and concise, making it good for absolute beginners. The hands-on labs are often cited as helpful for reinforcing learning. However, a significant number of students report that setting up the necessary virtual machine environment is challenging and frustrating, leading to troubleshooting time. Some also feel the course content and used software versions are becoming outdated, potentially making practical application difficult with current distributions. Reviewers note the title "Master Apache Hadoop" is a misleading exaggeration, as the course provides only a foundational overview, requiring further study for true mastery.

Lab exercises provide valuable practical experience.

"The hands-on labs reinforce learning effectively, once you get the environment set up."

"Lab exercises using the VM were valuable practice."

"The labs provide decent practice, though the VM setup part needs updating..."

"I found the lab exercises very helpful for hands-on learning."

Serves as a solid starting point for beginners.

"This course is a solid introduction to Hadoop concepts like HDFS and MapReduce. The explanations are clear for beginners."

"Excellent course for someone completely new to Big Data and Hadoop. The instructor breaks down complex topics into understandable parts."

"Fantastic introductory course! The instructor is clear and concise... Definitely recommend for beginners."

"I gained a good grounding in Hadoop fundamentals. HDFS and MapReduce sections were well explained."

Title 'Master' is an exaggeration for this intro.

"The title 'Master' is definitely an overstatement; this is strictly introductory."

"Content is superficial, not suitable for mastering anything."

"It's an okay foundation but doesn't live up to the 'Master' title at all."

"Don't expect to 'Master' Hadoop with this; it's just a basic intro."

Software versions and tools may be older.

"The content feels a bit dated in parts, but the core principles are still relevant."

"The course uses older versions of software and the setup labs for the VM were a nightmare."

"Some parts felt a bit out of sync with the latest Hadoop ecosystem tools."

"Outdated content and environment setup issues make this course hard to recommend."

Setting up the required virtual machine is difficult.

"However, setting up the Cloudera VM was quite challenging and time-consuming."

"The labs are helpful but the VM setup process is frustrating and prone to errors."

"I spent more time troubleshooting the environment than learning Hadoop."

"The VM setup was a major hurdle for me."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Apache Hadoop - Infinite Skills Hadoop Training with these activities:

Review Basic Linux Commands

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Reinforce your understanding of basic Linux commands, as Hadoop often runs on Linux-based systems and requires command-line interaction.

Browse courses on Hadoop

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Review common commands like ls, cd, mkdir, rm, cp, mv.
Practice navigating the file system using the terminal.
Familiarize yourself with file permissions and ownership.

Review: Hadoop: The Definitive Guide

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Deepen your understanding of Hadoop architecture and components by studying a comprehensive guide.

View Hadoop: The Definitive Guide: Storage and... on Amazon

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Read the chapters on HDFS and MapReduce.
Study the examples provided in the book.
Take notes on key concepts and terminology.

Practice HDFS Commands

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Solidify your understanding of HDFS by practicing common commands for file management and data access.

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Create directories and upload files to HDFS.
List files and directories in HDFS.
Download files from HDFS to your local machine.
Delete files and directories from HDFS.

Four other activities

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Create a Hadoop Cheat Sheet

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Consolidate your learning by creating a cheat sheet summarizing key Hadoop concepts, commands, and configurations.

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Identify the most important Hadoop concepts.
Summarize the key commands for HDFS and MapReduce.
Organize the information in a clear and concise format.
Share your cheat sheet with other students.

Simple Data Analysis with MapReduce

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Apply your knowledge of MapReduce by developing a simple data analysis application, such as word count or log analysis.

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Choose a dataset for analysis.
Write a MapReduce program to process the data.
Run the program on a Hadoop cluster.
Analyze the results and identify insights.

Review: Hadoop Operations

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Gain insights into Hadoop cluster management and operations by reviewing a dedicated guide.

View Hadoop Operations: A Guide for Developers and... on Amazon

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Read the chapters on cluster setup and configuration.
Study the sections on monitoring and troubleshooting.
Take notes on best practices for Hadoop operations.

Follow Advanced MapReduce Tutorials

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Extend your MapReduce skills by following tutorials on advanced topics such as partitioners, combiners, and custom input formats.

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Search for tutorials on advanced MapReduce techniques.
Implement the examples provided in the tutorials.
Experiment with different configurations and parameters.
Apply the techniques to your own data analysis projects.

Career center

Learners who complete Master Apache Hadoop - Infinite Skills Hadoop Training will develop knowledge and skills that may be useful to these careers:

Hadoop Developer

A Hadoop developer builds and maintains applications that run on the Hadoop platform. This course provides a solid introduction to the tools and functions needed for Hadoop development. The MapReduce code walkthrough helps build a foundational understanding of the Hadoop programming model. The debugging basics section and the coverage of Hive, Pig, and Impala improve development skills. Hands-on experience gained from the labs on installing Hadoop and using HDFS is highly valuable for a Hadoop developer, as is knowledge of importing and exporting data.

See salaries and explore the career path for Hadoop Developer

Data Engineer

A data engineer designs, builds, and manages the infrastructure required for data storage and processing. This course helps build a strong foundation in Hadoop, a crucial technology for handling large datasets. Data engineers leverage Hadoop's distributed file system (HDFS) and MapReduce for efficient data processing. Understanding Hadoop run modes and job types, as covered in this course, helps a data engineer optimize data workflows. The labs on installing Hadoop from CDH and using HDFS provide practical experience directly applicable to this role. Learning about data import and export options, including Flume and Sqoop, is also essential for a data engineer.

See salaries and explore the career path for Data Engineer

Big Data Architect

A big data architect designs and oversees the implementation of big data solutions for an organization. This course helps learn the core components of the Hadoop ecosystem, essential for any big data architecture. The course's exploration of HDFS architecture, MapReduce, Hive, Pig, and Impala are directly relevant. An understanding of Hadoop hardware requirements and the Hadoop approach to big data, covered early in this course, helps inform architectural decisions. The sections on data import and export options, including Flume, Sqoop, and Oozie, provide insights into data integration strategies.

See salaries and explore the career path for Big Data Architect

Data Scientist

Data scientists analyze large datasets to extract meaningful insights and inform business decisions. While data scientists use a variety of tools, this course helps understand how Hadoop can be used for data storage and processing, particularly when dealing with big data. The sections on Hive, Pig, and Impala provide tools for querying and transforming data within the Hadoop ecosystem. Further, the data import and export sections of this course help one learn how to get data into and out of Hadoop for analysis. A data scientist who knows big data technologies is more effective at their job.

See salaries and explore the career path for Data Scientist

ETL Developer

An extract, transform, load (ETL) developer designs and builds systems for extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse or other data storage system. This course helps learn about data import and export options in Hadoop, including Flume and Sqoop, which are commonly used for ETL processes. Understanding HDFS and MapReduce, as covered in the course, is also relevant for building efficient data pipelines. ETL Developers who know Hadoop are more effective at their job.

See salaries and explore the career path for ETL Developer

Data Warehouse Manager

This course helps a data warehouse manager learn about Hadoop, including HDFS, MapReduce, Hive, and Pig. Managing a hybrid data warehouse or integrating Hadoop into its architecture may be necessary for a data warehouse manager, and this course provides a foundational understanding. This helps in the design of efficient ETL processes for populating the data warehouse with data from diverse sources.

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Database Administrator

A database administrator (DBA) manages and maintains database systems, ensuring their performance, security, and availability. For organizations using Hadoop, DBAs may need to manage and optimize Hadoop clusters. This course is designed for absolute beginners and helps build a foundational understanding of Hadoop architecture and its components. The sections on HDFS architecture, access controls, and data import/export are particularly relevant for a DBA. The labs on installing Hadoop and using HDFS provide hands-on experience with Hadoop cluster management.

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Cloud Solutions Architect

A cloud solutions architect designs and implements cloud-based solutions for organizations. This course may be useful because it discusses Hadoop in the cloud using Amazon Web Services. This introduction helps understand how to deploy and manage Hadoop clusters in a cloud environment. Understanding HDFS and MapReduce, as covered in the course, are relevant when designing data processing pipelines in the cloud. A cloud solutions architect must know Hadoop to be effective.

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Solutions Architect

A solutions architect designs and implements technology solutions for business problems. This course helps a solutions architect learn how to leverage Hadoop for big data processing and storage. Understanding the Hadoop ecosystem, HDFS, MapReduce, Hive, Pig, and Impala, may be useful when designing solutions that involve large-scale data analysis. This course assists in the design of scalable and efficient data architectures.

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Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends and insights that inform business decisions. While this role typically involves using tools like SQL and visualization software, this course may be useful in understanding how Hadoop can be used to process and store large datasets that feed into BI systems. The sections on Hive, Pig, and Impala provide tools for querying and transforming data within Hadoop. Understanding data import and export options, including Sqoop, is useful for integrating Hadoop with existing BI infrastructure.

See salaries and explore the career path for Business Intelligence Analyst

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models. This course may be useful in understanding how Hadoop can be used to store and process the large datasets required for training machine learning models. The sections on HDFS architecture, MapReduce, and data import/export provide a foundation for working with data in a distributed environment. Machine learning engineers can use Hadoop to preprocess and transform data before feeding it into machine learning algorithms.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts interpret data, analyze results using statistical techniques and provide ongoing reports. While this role typically involves using tools like SQL, Excel, and visualization software, this course may be useful in understanding how Hadoop can be used to process and store large datasets. The section discussing Hive, Pig, and Impala helps one query and transform data within Hadoop. Further, the data import and export sections of this course helps one learn how to get data into and out of Hadoop for analysis.

See salaries and explore the career path for Data Analyst

Software Developer

Software developers design, develop, and test software applications. This course may be useful for software developers who need to work with big data or integrate their applications with Hadoop. Understanding the Hadoop ecosystem, including HDFS, MapReduce, Hive, and Pig, helps a developer build applications that can process large volumes of data. The course's focus on the basics of debugging is also beneficial for writing robust and reliable code.

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Information Security Analyst

An information security analyst is responsible for protecting an organization's data and systems from cyber threats. This course helps one learn about HDFS access controls, which are essential for securing data stored in a Hadoop cluster. The information security analyst benefits from this course.

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System Administrator

System administrators are responsible for maintaining and managing computer systems and servers. This course may be useful because it helps one understand the hardware requirements for Hadoop and how to install Hadoop from CDH using Cloudera Manager. This knowledge helps a system administrator manage Hadoop clusters in a data center or cloud environment. Knowing Hadoop is valuable for a system administrator.

See salaries and explore the career path for System Administrator

Master Apache Hadoop - Infinite Skills Hadoop Training

What's inside

Syllabus

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Reviews summary

Solid introduction with setup hurdles

Activities

Career center

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