Big Data Analysis with Scala and Spark

Go to Course: https://www.coursera.org/learn/scala-spark-big-data

Introduction

### Course Review: Big Data Analysis with Scala and Spark on Coursera In today’s data-driven world, the ability to manipulate and analyze big data is an invaluable skill that can set you apart in the competitive job market. The Coursera course, **"Big Data Analysis with Scala and Spark,"** provides a comprehensive platform for learners who wish to delve deep into big data processing frameworks, particularly using the powerful combination of Scala and Apache Spark. #### Overview This course stands out as it emphasizes functional programming concepts for big data manipulation on distributed systems. The course draws on the widespread industrial dependence on tools like MapReduce and even more so on Apache Spark, which has revolutionized big data processing with its speed and capability for in-memory data handling. By adopting Scala, a language known for its functional programming capabilities, learners are introduced to a model that is not only efficient but also highly relevant in industries that thrive on data analytics. #### Syllabus Breakdown The course is divided into several engaging and informative modules that gradually build your knowledge in big data processing. Here’s a brief overview of what you can expect: 1. **Getting Started + Spark Basics**: - Kickoff your journey by setting up Scala on your machine and completing a hands-on assignment. This week effectively bridges the concepts of data parallelism in shared memory with those in distributed environments. You will gain insight into Spark fundamentals and the challenges of distributed computing, such as latency and failures. Finishing the week with a real-world data set analysis sets a practical tone for the learning experience. 2. **Reduction Operations & Distributed Key-Value Pairs**: - This module introduces pair RDDs—an essential component that allows you to perform reductions and joins on large datasets. Understanding these operations is critical for manipulating data effectively in a distributed environment. 3. **Partitioning and Shuffling**: - Here, the focus is on performance, particularly the implications of operations like joins. You'll learn about data partitioning to enhance data locality, which can significantly optimize Spark job performance. 4. **Structured Data: SQL, Dataframes, and Datasets**: - This week emphasizes structured data utilization to achieve optimal performance in Spark jobs. You'll explore Spark SQL and its optimizer, then dive into DataFrames and Datasets, enhancing your ability to leverage automatic optimizations while working seamlessly with RDDs. #### Pros - **Hands-On Learning**: The course's project-based approach ensures that you immediately apply what you learn, making concepts more tangible. - **Relevant Content**: With a solid focus on industry practices and current big data technologies, the course prepares you for real-world challenges. - **Strong Foundation**: It builds a solid foundation in both Scala and Spark, complete with a deep understanding of distributed data processing. #### Cons - **Prerequisite Knowledge**: The course requires a preliminary understanding of parallel programming concepts. For complete beginners, this might present a challenge. - **Pace**: The depth of the content may be demanding, especially for those who are new to functional programming or big data analysis. #### Recommendation I highly recommend the **"Big Data Analysis with Scala and Spark"** course for anyone looking to enhance their skills in data analysis and big data technologies. Whether you are a data scientist, a developer, or simply someone passionate about learning, this course provides you with the tools and knowledge to excel in the field of big data. By the end of the course, not only will you feel empowered by your newfound abilities, but you will also be well-equipped to tackle data-centric challenges in a multitude of professional settings. Don’t miss out on this opportunity to elevate your career with one of the most sought-after skill sets in today’s job market! ### Conclusion If you are enthusiastic about big data, consider investing your time in this course. With its structured modules and practical approach, **"Big Data Analysis with Scala and Spark"** will undoubtedly pave your way towards becoming a proficient data analyst capable of leveraging cutting-edge technologies.

Syllabus

Getting Started + Spark Basics

Get up and running with Scala on your computer. Complete an example assignment to familiarize yourself with our unique way of submitting assignments. In this week, we'll bridge the gap between data parallelism in the shared memory scenario (learned in the Parallel Programming course, prerequisite) and the distributed scenario. We'll look at important concerns that arise in distributed systems, like latency and failure. We'll go on to cover the basics of Spark, a functionally-oriented framework for big data processing in Scala. We'll end the first week by exercising what we learned about Spark by immediately getting our hands dirty analyzing a real-world data set.

This week, we'll look at a special kind of RDD called pair RDDs. With this specialized kind of RDD in hand, we'll cover essential operations on large data sets, such as reductions and joins.

This week we'll look at some of the performance implications of using operations like joins. Is it possible to get the same result without having to pay for the overhead of moving data over the network? We'll answer this question by delving into how we can partition our data to achieve better data locality, in turn optimizing some of our Spark jobs.

With our newfound understanding of the cost of data movement in a Spark job, and some experience optimizing jobs for data locality last week, this week we'll focus on how we can more easily achieve similar optimizations. Can structured data help us? We'll look at Spark SQL and its powerful optimizer which uses structure to apply impressive optimizations. We'll move on to cover DataFrames and Datasets, which give us a way to mix RDDs with the powerful automatic optimizations behind Spark SQL.