Hadoop Platform and Application Framework

Go to Course: https://www.coursera.org/learn/hadoop

Introduction

**Course Review: Hadoop Platform and Application Framework on Coursera** In today's data-driven world, the ability to effectively manage and analyze big data is not just a valuable skill—it's a necessity. For novice programmers or business professionals eager to delve into the world of big data, the **Hadoop Platform and Application Framework** offered on Coursera stands as an excellent gateway. This course combines comprehensive theoretical knowledge with practical, hands-on experience using two of the most widely adopted frameworks in the industry: Hadoop and Spark. ### Course Overview The **Hadoop Platform and Application Framework** is tailored for individuals with little to no prior exposure to big data tools. It serves as an introduction to the core technologies that underpin big data analytics. Over the duration of the course, participants will not only gain familiarity with the terminologies but will also become adept at explaining critical components and processes of the Hadoop architecture, software stack, and execution environment. ### Syllabus Breakdown 1. **Hadoop Basics** The course kicks off with an engaging overview of the big data landscape. In this module, learners are introduced to the concept of Big Data, its accompanying hype, opportunities, and challenges. This foundational understanding sets the stage for the detailed exploration of the Hadoop stack. 2. **Introduction to the Hadoop Stack** Here, participants delve deeper, examining the various components of the Hadoop stack—from HDFS (Hadoop Distributed File System) to application execution frameworks and services. This module provides a robust understanding of how different pieces fit together to form a cohesive big data solution. 3. **Introduction to Hadoop Distributed File System (HDFS)** HDFS is a key component of Hadoop, and understanding its intricacies is crucial. This module focuses on HDFS's design goals, the read/write process, and how to configure it for optimal performance. Additionally, learners will explore the different methods of data access within HDFS. 4. **Introduction to Map/Reduce** The Map/Reduce paradigm is fundamental in big data processing. This section introduces essential concepts, guiding students through the design, implementation, and execution of tasks within the Map/Reduce framework. It also discusses the trade-offs associated with Map/Reduce while introducing tools that emerged as alternatives. 5. **Spark** The final module covers Apache Spark, a powerful big data processing framework that offers significant performance enhancements over traditional Map/Reduce. This section highlights Spark's advantages, particularly in iterative algorithms and its user-friendly platforms for data scientists. Participants will learn how to utilize Spark’s capabilities with Python and Scala, and even engage in interactive data analysis. ### Review Highlights **Pros:** - **Accessible Content**: The course is designed for those new to programming and big data, making complex concepts digestible and approachable. - **Hands-on Learning**: The inclusion of practical assignments enables students to experiment and solidify their understanding of the material. - **Industry Relevance**: With a strong focus on Hadoop and Spark, learners gain exposure to tools widely used in the industry, enhancing their employability. **Cons:** - **Pace**: Some participants may find the pace of certain modules challenging, especially if they don't have a technical background. However, the course encourages learning at one's own pace, allowing users to revisit materials as needed. ### Recommendations I highly recommend the **Hadoop Platform and Application Framework** for anyone looking to make their mark in the big data landscape. Whether you are a novice programmer or a business professional keen on understanding big data tools, this comprehensive course provides the foundational knowledge and practical skills necessary to navigate and analyze vast datasets. The insights gained from this course not only enrich your understanding but also empower you to tackle real-world challenges associated with big data. By the end of the course, you will undoubtedly feel more confident discussing and utilizing the Hadoop and Spark frameworks in any professional context. Enroll now, and embark on your journey to becoming a big data proficient!

Syllabus

Hadoop Basics

Welcome to the first module of the Big Data Platform course. This first module will provide insight into Big Data Hype, its technologies opportunities and challenges. We will take a deeper look into the Hadoop stack and tool and technologies associated with Big Data solutions.

In this module we will take a detailed look at the Hadoop stack ranging from the basic HDFS components, to application execution frameworks, and languages, services.

In this module we will take a detailed look at the Hadoop Distributed File System (HDFS). We will cover the main design goals of HDFS, understand the read/write process to HDFS, the main configuration parameters that can be tuned to control HDFS performance and robustness, and get an overview of the different ways you can access data on HDFS.

This module will introduce Map/Reduce concepts and practice. You will learn about the big idea of Map/Reduce and you will learn how to design, implement, and execute tasks in the map/reduce framework. You will also learn the trade-offs in map/reduce and how that motivates other tools.

Welcome to module 5, Introduction to Spark, this week we will focus on the Apache Spark cluster computing framework, an important contender of Hadoop MapReduce in the Big Data Arena. Spark provides great performance advantages over Hadoop MapReduce,especially for iterative algorithms, thanks to in-memory caching. Also, gives Data Scientists an easier way to write their analysis pipeline in Python and Scala,even providing interactive shells to play live with data.