Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

Go to Course: https://www.coursera.org/learn/cloud-applications-part2

Introduction

### Course Review: Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud In today's data-driven world, understanding the intricacies of cloud computing and big data is becoming increasingly essential. Coursera’s **Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud** stands out as a vital resource for anyone looking to gain comprehensive insights into how these technologies can transform analytics and data processing. #### Course Overview This course is the second installment in a two-part series that delves into cloud computing and its intersection with big data analytics. While the first part provides foundational knowledge, this course advances your understanding by exploring how cloud technologies facilitate the analysis of vast volumes of data, whether static or in high-velocity streams. #### Syllabus Breakdown The course consists of four modules, each meticulously crafted to build on the learners' knowledge and skills: 1. **Course Orientation**: This initial segment is crucial as it familiarizes you with the learning platform, your peers, and the technical skills you'll need throughout the course. This orientation lays the groundwork for a smooth learning experience. 2. **Module 1: Spark, Hortonworks, HDFS, CAP**: You’ll begin your journey into the realm of Big Data applications with Apache Spark, an essential framework for data processing. The module covers the HDFS file system and the MapReduce paradigm, two cornerstones of batch-based data processing. This introduction sets the stage for understanding how data can be efficiently harnessed in the cloud. 3. **Module 2: Large Scale Data Storage**: Here, you'll dive into various large-scale storage technologies, addressing the challenges of managing extensive datasets in distributed systems. The module encompasses in-memory storage, NoSQL databases, and other frameworks, giving you a well-rounded understanding crucial for real-world applications. 4. **Module 3: Streaming Systems**: In this dynamic segment, you'll explore real-time data processing. The technologies covered include Apache Storm and Spark Streaming, with discussions around Lambda and Kappa architectures. The real-time aspect of data handling is vital for modern applications, and this module equips you with the necessary knowledge. 5. **Module 4: Graph Processing and Machine Learning**: The final module looks at the broad applications of big data, focusing on graph processing and machine learning. You’ll learn about handling massive graphical datasets and how machine learning models can be trained using extensive data. With introductions to deep learning techniques, this module allows you to appreciate the transformative power of big data in business and technology. #### Why You Should Enroll **Practical Skills Development**: Each module of the course is designed to equip you with practical skills that are highly relevant in today’s job market. The hands-on approach ensures that you can translate what you learn into real-world applications. **Industry-Relevant Content**: Given the fast-paced evolution of cloud technologies and big data analytics, this course offers up-to-date knowledge that is directly applicable to various industries including finance, healthcare, and technology. **Community and Support**: Coursera's platform encourages interaction with fellow learners, providing opportunities for networking, collaboration, and exchange of ideas, which can enhance your learning experience. **Certificate of Completion**: Upon finishing the course, you'll receive a certificate that adds value to your resume, showcasing your commitment to staying current in a rapidly advancing field. ### Recommendation For anyone interested in data analytics, cloud computing, or those seeking to deepen their technical expertise in big data applications, **Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud** is an invaluable educational endeavor. This course is essential—not just for aspirants and fresh graduates, but also for professionals looking to upskill and expand their knowledge in cloud computing and big data analytics. Enroll, and elevate your understanding of this pivotal technological frontier!

Syllabus

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

In Module 1, we introduce you to the world of Big Data applications. We start by introducing you to Apache Spark, a common framework used for many different tasks throughout the course. We then introduce some Big Data distro packages, the HDFS file system, and finally the idea of batch-based Big Data processing using the MapReduce programming paradigm.

In this module, you will learn about large scale data storage technologies and frameworks. We start by exploring the challenges of storing large data in distributed systems. We then discuss in-memory key/value storage systems, NoSQL distributed databases, and distributed publish/subscribe queues.

This module introduces you to real-time streaming systems, also known as Fast Data. We talk about Apache Storm in length, Apache Spark Streaming, and Lambda and Kappa architectures. Finally, we contrast all these technologies as a streaming ecosystem.

In this module, we discuss the applications of Big Data. In particular, we focus on two topics: graph processing, where massive graphs (such as the web graph) are processed for information, and machine learning, where massive amounts of data are used to train models such as clustering algorithms and frequent pattern mining. We also introduce you to deep learning, where large data sets are used to train neural networks with effective results.