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"Apache Spark Hands on Specialization for Big Data Analytics"-Ref.:Udemy.com, Apache Spark, Big Data Analytics, Apache, Spark



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"Apache Spark Hands on Specialization for Big Data Analytics"-Ref.:Udemy.com

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" * In-depth course to master Apache Spark Development using Scala for Big Data (with 30+ real-world & hands-on examples) * * What Will I Learn? * Understand the relationship between Apache Spark and Hadoop Ecosystem * Understand Apache Spark use-cases and advanced characteristics * Understand Apache Spark Architecture and how it works * Understand how Apache Spark on YARN (Hadoop) works in multiple modes * Understand development life-cycle of Apache Spark Applications in Python and Scala * Learn the foundations of Scala programming language * Understand Apache Spark's primary data abstraction (RDDs) * Understand and use RDDs advanced characteristics (e.g. partitioning) * Learn nuances in loading files in Hadoop Distributed File system in Apache Spark * Learn implications of delimiters in text files and its processing in Spark * Create and use RDDs by parallelizing Scala's collection objects and implications * Learn the usage of Spark and YARN Web UI to gain in-depth operational insights * Understand Spark's Direct Acyclic Graph (DAG) based execution model and implications * Learn Transformations and their lazy execution semantics * Learn Map transformation and master its applications in real-world challenges * Learn Filter transformation and master its usage in real-world challenges * Learn Apache Spark's advanced Transformations and Actions * Learn and use RDDs of different JVM objects including collections and understanding critical nuances * Learn and use Apache Spark for statistical analysis * Learn and master Key Value Pair RDDs and their applications in complex Big Data problems * Learn and master Join Operations on complex Key Value Pair RDDs in Apache Spark * Learn how RDDs caching works and use it for advanced performance optimization * Learn how to use Apache Spark for Data Ranking problems * Learn how to use Apache Spark for handling and processing structured and unstructured data * Learn how to use Apache Spark for advanced Business Analytics * Learn how to use Apache Spark for advanced data integrity and quality checks * Learn how to use Scala's advanced features like functional programming and pattern matching * Learn how to use Apache Spark for logs processing * Requirements * Background knowledge of Big Data would be helpful but not necessary as everything will be taught from scratch * Past experience of programming language would be helpful but not necessary as everything will be taught from scratch * A computer system (Laptop/Desktop) with either Windows, Linux or Mac installed for hands-on practice * All the software and tools used are freely available * The most important requirement: Thirst and commitment to learn! * Description * What if you could catapult your career in one of the most lucrative domains i.e. Big Data by learning the state of the art Hadoop technology (Apache Spark) which is considered mandatory in all of the current jobs in this industry? * * What if you could develop your skill-set in one of the most hottest Big Data technology i.e. Apache Spark by learning in one of the most comprehensive course out there (with 10+ hours of content) packed with dozens of hands-on real world examples, use-cases, challenges and best-practices? * * What if you could learn from an instructor who is working in the world's largest consultancy firm, has worked, end-to-end, in Australia's biggest Big Data projects to date and who has a proven track record on Udemy with highly positive reviews and thousands of students already enrolled in his previous course(s)? * * If you have such aspirations and goals, then this course and you is a perfect match made in heaven! * * Why Apache Spark? * * Apache Spark has revolutionised and disrupted the way big data processing and machine learning were done by virtue of its unprecedented in-memory and optimised computational model. It has been unanimously hailed as the future of Big Data. It's the tool of choice all around the world which allows data scientists, engineers and developers to acquire and process data for a number of use-cases like scalable machine learning, stream processing and graph analytics to name a few. All of the leading organisations like Amazon, Ebay, Yahoo among many others have embraced this technology to address their Big Data processing requirements. * * Additionally, Gartner has repeatedly highlighted Apache Spark as a leader in Data Science platforms. Certification programs of Hadoop vendors like Cloudera and Hortonworks, which have high esteem in current industry, have oriented their curriculum to focus heavily on Apache Spark. Almost all of the jobs in Big Data and Machine Learning space demand proficiency in Apache Spark. * * This is what John Tripier, Alliances and Ecosystem Lead at Databricks has to say, “The adoption of Apache Spark by businesses large and small is growing at an incredible rate across a wide range of industries, and the demand for developers with certified expertise is quickly following suit”. * * All of these facts correlate to the notion that learning this amazing technology will give you a strong competitive edge in your career. * * Why this course? * * Firstly, this is the most comprehensive and in-depth course ever produced on Apache Spark. I've carefully and critically surveyed all of the resources out there and almost all of them fail to cover this technology in the depth that it truly deserves. Some of them lack coverage of Apache Spark's theoretical concepts like its architecture and how it works in conjunction with Hadoop, some fall short in thoroughly describing how to use Apache Spark APIs optimally for complex big data problems, some ignore the hands-on aspects to demonstrate how to do Apache Spark programming to work on real-world use-cases and almost all of them don't cover the best practices in industry and the mistakes that many professionals make in field. * * This course addresses all of the limitations that's prevalent in the currently available courses. Apart from that, as I have attended trainings from leading Big Data vendors like Cloudera (for which they charge thousands of dollars), I've ensured that the course is aligned with the educational patterns and best practices followed in those training to ensure that you get the best and most effective learning experience. * * Each section of the course covers concepts in extensive detail and from scratch so that you won't find any challenges in learning even if you are new to this domain. Also, each section will have an accompanying assignment section where we will work together on a number of real-world challenges and use-cases employing real-world data-sets. The data-sets themselves will also belong to different niches ranging from retail, web server logs, telecommunication and some of them will also be from Kaggle (world's leading Data Science competition platform). * * The course leverages Scala instead of Python. Even though wherever possible, reference to Python development is also given but the course is majorly based on Scala. The decision was made based on a number of rational factors. Scala is the de-facto language for development in Apache Spark. Apache Spark itself is developed in Scala and as a result all of the new features are initially made available in Scala and then in other languages like Python. Additionally, there is significant performance difference when it comes to using Apache Spark with Scala compared to Python. Scala itself is one of the most highest paid programming languages and you will be developing strong skill in that language along the way as well. * * The course also has a number of quizzes to further test your skills. For further support, you can always ask questions to which you will get prompt response. I will also be sharing best practices and tips on regular basis with my students. * * What you are going to learn in this course? * * The course consists of majorly two sections: * * Section - 1: * We'll start off with the introduction of Apache Spark and will understand its potential and business use-cases in the context of overall Hadoop ecosystem. We'll then focus on how Apache Spark actually works and will take a deep dive of the architectural components of Spark as its crucial for thorough understanding. * * Section - 2: * After developing understanding of Spark architecture, we will move to the next section of this course where we will employ Scala language to use Apache Spark APIs to develop distributed computation programs. Please note that you don't need to have prior knowledge of Scala for this course as I will start with the very basics of Scala and as a result you will also be developing your skills in this one of the highest paying programming languages. * * In this section, We will comprehensively understand how spark performs distributed computation using abstractions like RDDs, what are the caveats in loading data in Apache Spark, what are the different ways to create RDDs and how to leverage parallelism and much more. * * Furthermore, as transformations and action constitute the gist of Apache Spark APIs thus its imperative to have sound understanding of these. Thus, we will then focus on a number of Spark transformations and Actions that are heavily being used in Industry and will go into detail of each. Each API usage will be complimented with a series of real-world examples and datasets e.g. retail, web server logs, customer churn and also from kaggle. Each section of the course will have a number of assignments where you will be able to practically apply the learned concepts to further consolidate your skills. * * A significant section of the course will also be dedicated to key value RDDs which form the basis of working optimally on a number of big data problems. * * In addition to covering the crux of Spark APIs, I will also highlight a number of valuable best practices based on my experience and exposure and will also intuit on mistakes that many people do in field. You will rarely such information anywhere else. * * Each topic will be covered in a lot of detail with strong emphasis on being hands-on thus ensuring that you learn Apache Spark in the best possible way. * * The course is applicable and valid for all versions of Spark i.e. 1.6 and 2.0. * * After completing this course, you will develop a strong foundation and extended skill-set to use Spark on complex big data processing tasks. Big data is one of the most lucractive career domains where data engineers claim salaries in high numbers. This course will also substantially help in your job interviews. Also, if you are looking to excel further in your big data career, by passing Hadoop certifications like of Cloudera and Hortonworks, this course will prove to be extremely helpful in that context as well. * * Lastly, once enrolled, you will have life-time access to the lectures and resources. Its a self-paced course and you can watch lecture videos on any device like smartphone or laptop. Also, you are backed by Udemy's rock-solid 30 days money back guarantee. So if you are serious about learning about learning Apache Spark, enrol in this course now and lets start this amazing journey together! * * Who is the target audience? * Anyone who has the passion to develop expertise in Big Data and specifically Apache Spark * Software Engineers or Developers * Data Warehousing or Business Intelligence Professionals * Data Scientist and Machine Learning Enthusiasts * Data Engineers and Big Data Architects * Curriculum For This Course - Lectures * Introduction * Breaking the Ice with Warm Welcome! * Course's Curriculum - Journey to the excellence! * Section 1 - Apache Spark Introduction and Architecture Deep Dive * Apache Spark in the context of Hadoop Evolution * Say Hello to Apache Spark - Thorough Dissemination of Capabilities * Working with Text Files to create Resilient Distributed Datasets (RDDs) in Spark * Setting up development Environment * Loading Text Files (in HDFS) in Spark to create RDDs * Loading All Directory Files (in HDFS) simultaneously in Spark and implications * Loading Text Files (in HDFS) in Spark - Continued * Using Wildcards to selectively load text files (in HDFS) in Spark and use-cases * Real Life Challenge: Different Record Delimiters in Text Files in Spark * Solution: Handling Different Record Delimiters in Text Files in Spark * T1 * Creating RDDs by Distributing Scala Collections in Spark * The semantics and implications behind parallelizing Scala Collections * Hands-on: Distributing/Parallelizing Scala Collections * Understanding the Partitioning and Distributed Nature of RDDs in Spark * How Data gets Partitioned and Distributed in Spark Cluster * Accessing Hadoop YARN RM and AM Web UIs to understand RDDs Partitioning * Manually Changing Partitions of RDDs in Spark and Implications * Developing Mastery in Spark's Map Transformations and lazy DAG Execution Model * Demystifying Spark's Direct Acyclic Graph (DAG) and Lazy Execution Model * Introducing Map Transformation - the Swiss Army Knife of Transformations * Hands-on: Map Transformation via Scala's Functional Programming constructs * Understanding the Potential of Map Transformation to alter RDDs Types * Using Your Own Functions, in addition to Anonymous ones, in Map Transformations * Assignment - Using Map Transformation on Real World Big Data Retail Analytics * Introducing the Real World Online Retail Data-set and Assignment Challenges * Detailed Hands-on Comprehension of Assignment Challenges' Solutions * Conceptual Understanding of Distributing Scala Collections and Implications * Hands-on Understanding of Distributing Scala Collections and use-cases * Developing Mastery in Spark's Filter Transformation * Introducing Filter Transformation and its Powerful Use-Cases * Hands on: Spark's Filter Transformation in Action * Assignment - Using Filter and Map on Apache Web Server Logs and Retail Dataset * Introducing the Data-sets and Real-World Assignment Challenges * Challenge 1: Removing Empty Lines in Web Logs Data-set * Challenge 2: Removing Header Line in Retail Data-set * Challenge 3: Selecting rows in Retail Data-set Containing Specific Countries * Developing Mastery in RDD of Scala Collections * Introducing RDDs of Scala Collections and their Relational Analytics use-cases * Transforming Scala Collections using Functional Programming Constructs * Creating and Manipulating RDDs of Arrays of String from Different Data Sources * Assignment - Customer Churn Analytics using Apache Spark * Introducing the Context, Challenges and Data-set of Customer Churn Use-Case * Challenge 1: Finding Number of Unique States in the Data-set * Challenge 2: Performing Data Integrity Check on Individual Columns of Data-Set * Challenge 3: Finding Summary Statistics on number of Voice Mail Messages * Challenge 4: Finding Summary Statistics on Voice Mail in Selected States * Challenge 5: Finding Average Value of Total Night Calls Minutes * Challenge 6: Finding conditioned Total day calls for customers * Challenge 7: Using Scala Functions and Pattern Matching for advanced processing * Challenge 8: Finding Churned Customers with International and Voice Mail Plan * Challenge 9: Performing Data Quality and Type Checks on Individual Columns * Developing Mastery in Spark's Key-Value (Pair) RDDs * Introduction * Developing Intuition for Solving Big Data Problems using KeyValue Pair Construct * Developing Hands-on Understanding of working with KeyValue RDDs in Spark * Proof - Transformations' exclusivity to KeyValue RDDs * Transforming Text File Data to Pair RDDs for KeyValue based Data Processing * The Case of Different Data Types of "Values" in KeyValue RDDs * Transforming Complex Delimited Text File to Pair RDDs for KeyValue Processing * Assignment - Analyzing Video Games (Kaggle Dataset) using Spark's KeyValue RDDs * Challenge 1: Determining Frequency Distribution of Video Games Platforms * Challenge 2: Finding Total Sales of Each Video Games Platform * Challenge 3: Finding Global Sales of Video Games Platform * Challenge 4: Maximum Sales Value of Each Gaming Console * Challenge 5: Data Ranking - Top 10 platforms by global sales * Developing Mastery in Join Operations on Key Value Pair RDDs in Apache Spark * Introducing Join Operations on Relational Data with Examples * Getting started with join operation in Spark with Key Value Pair RDDs * Working towards complex Join Operations in Apache Spark with advanced indexing * Assignment - A Real Life Relational Dataset about Retail Customers * Setting context and developing understanding of relationships in the dataset * Challenge 1 - Top 5 states with Most Orders' Status as Cancelled * Challenge 2 - Top 5 Cities from CA State with Orders Status as Cancelled * Apache Spark - Advanced Concepts * Introducing Caching in RDDs, Motivation and Relation to DAG Based Execution * Caching and Persistence in RDDs in Action " - Ref.: Udemy.com

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