Introduction to Big Data with Spark and Hadoop

Go to Course: https://www.coursera.org/learn/introduction-to-big-data-with-spark-hadoop

Introduction

### Course Review: Introduction to Big Data with Spark and Hadoop on Coursera **Overview** In today's data-driven world, understanding big data is no longer optional but essential for anyone looking to thrive in the tech industry. “Introduction to Big Data with Spark and Hadoop,” a self-paced course offered by IBM on Coursera, provides a robust foundation for learners interested in mastering the complexities of big data analytics. The course seamlessly combines theoretical concepts with practical applications, making it an attractive option for beginners and those looking to upskill. The course begins with a comprehensive overview of big data, explaining its characteristics and significance as outlined by Bernard Marr. With a focus on hands-on experiences using industry-standard tools like Apache Hadoop and Apache Spark, this course undeniably sets participants up for success in the field of big data analytics. **Course Content and Syllabus** The course is divided into several modules, each addressing critical facets of big data analytics: 1. **What Is Big Data?** - This module sets the stage by providing a clear definition of big data, its importance in various applications, and the key concepts utilized in big data analytics such as parallel processing and scaling. Moreover, learners will explore real-world use cases, which helps in contextualizing the information. 2. **Introduction to the Hadoop Ecosystem** - Here, learners delve into Apache Hadoop's architecture and ecosystem. Through engaging hands-on labs, participants gain practical skills in querying data with Hive and launching a Hadoop cluster using Docker, solidifying their technical understanding. 3. **Apache Spark** - This module shifts focus to Apache Spark, a highly regarded platform for big data processing. Essential topics such as functional programming, resilient distributed datasets (RDDs), and Spark's capabilities are covered. The exploration of Spark's relationship with SQL provides a comprehensive understanding of structured data processing. 4. **DataFrames and Spark SQL** - Building on prior knowledge, this module introduces DataFrames, allowing learners to compare and contrast with RDDs. Participants also learn important optimization techniques in Spark SQL through guided labs, enhancing their practical skill set. 5. **Development and Runtime Environment Options** - This module focuses on the operational aspects of Spark applications, including configuration, submission, and dependency management. Hands-on labs teaching how to use Spark on IBM Cloud and Kubernetes give learners valuable experience in real-world environments. 6. **Monitoring and Tuning** - With the infinite potential of big data comes the necessity for monitoring and tuning. This module emphasizes tools and techniques for managing Spark applications and diagnosing issues through the Spark Application UI. 7. **Final Project and Assessment** - Rounding off the course, learners engage in a practical lab where they work on RDDs and DataFrames. The culmination of the course is a final project that allows students to apply everything they’ve learned, reinforcing their knowledge and skills. **Recommendation** I wholeheartedly recommend “Introduction to Big Data with Spark and Hadoop” for anyone eager to deepen their understanding of big data analytics. The course's structure, which balances theory and practical application, engages learners and enhances comprehension. The knowledge gained is not only timely but also highly valuable, given the increasing reliance on big data in business decision-making. Whether you are a student seeking to enter the tech field, a professional aiming to enhance your career prospects, or simply an enthusiast wanting to grasp the essentials of big data, this course is tailored for you. The self-paced nature of the course ensures flexibility, allowing you to learn at your convenience. By the end of this course, you will not only have a solid understanding of big data technologies but also the confidence to leverage them in real-world scenarios. Don't miss out on this opportunity to boost your skills and knowledge in the ever-evolving world of big data analytics!

Syllabus

What Is Big Data?

In this module, you’ll begin your acquisition of Big Data knowledge with the most up-to-date definition of Big Data. You’ll explore the impact of Big Data on everyday personal tasks and business transactions with Big Data Use Cases. You’ll also learn how Big Data uses parallel processing, scaling, and data parallelism. Going further, you’ll explore commonly used Big Data tools and explain the role of open-source in Big Data. Finally, you’ll go beyond the hype and explore additional Big Data viewpoints.

In this module, you'll gain a fundamental understanding of the Apache Hadoop architecture, ecosystem, practices, and commonly used applications, including Distributed File System (HDFS), MapReduce, Hive, and HBase. You’ll also gain practical skills in hands-on labs when you query the data added using Hive, launch a single-node Hadoop cluster using Docker, and run MapReduce jobs.

In this module, you’ll turn your attention to the popular Apache Spark platform, where you will explore the attributes and benefits of Apache Spark and distributed computing. You'll gain key insights about functional programming and Lambda functions. You’ll also explore Resilient Distributed Datasets (RDDs), parallel programming, resilience in Apache Spark, and relate RDDs and parallel programming with Apache Spark. Then, you’ll dive into additional Apache Spark components and learn how Apache Spark scales with Big Data. Working with Big Data signals the need for working with queries, including structured queries using SQL. You’ll also learn about the functions, parts, and benefits of Spark SQL and DataFrame queries, and discover how DataFrames work with Spark SQL.

In this module, you’ll learn about Resilient Distributed Datasets (RDDs), their uses in Apache Spark, and RDD transformations and actions. You'll compare the use of datasets with Spark's latest data abstraction, DataFrames. You'll learn to identify and apply basic DataFrame operations. You’ll explore Apache Spark SQL optimization and learn how Spark SQL and memory optimization benefit from using Catalyst and Tungsten. Finally, you’ll fortify your skills with guided hands-on lab to create a table view and apply data aggregation techniques.

In this module, you’ll explore how Spark processes the requests that your application submits and learn how you can track work using the Spark Application UI. Because Spark application work happens on the cluster, you need to be able to identify Apache Cluster Managers, their components, and benefits. You’ll also know how to connect with each cluster manager and how and when you might want to set up a local, standalone Spark instance. Next, you’ll learn about Apache Spark application submission, including the use of Spark’s unified interface, “spark-submit,” and learn about options and dependencies. You’ll also describe and apply options for submitting applications, identify external application dependency management techniques, and list Spark Shell benefits. You’ll also look at recommended practices for Spark's static and dynamic configuration options and perform hands-on labs to use Apache Spark on IBM Cloud and run Spark on Kubernetes.

Platforms and applications require monitoring and tuning to manage issues that inevitably happen. In this module, you'll learn about connecting the Apache Spark user interface web server and using the same UI web server to manage application processes. You’ll also identify common Apache Spark application issues and learn about debugging issues using the application UI and locating related log files. Further, you’ll discover and gain real-world knowledge about how Spark manages memory and processor resources using the hands-on lab.

In this module, you’ll perform a practice lab where you’ll explore two critical aspects of data processing using Spark: working with Resilient Distributed Datasets (RDDs) and constructing DataFrames from JSON data. You will also apply various transformations and actions on both RDDs and DataFrames to gain insights and manipulate the data effectively. Further, you’ll apply your knowledge in a final project where you will create a DataFrame by loading data from a CSV file and applying transformations and actions using Spark SQL. Finally, you’ll be assessed based on your learning from the course.