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آموزش کامل آنالیز و تحلیل بیگ دیتا با Apache Spark

دانلود Udemy Apache Spark Hands on Specialization for Big Data Analytics

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در این دوره آموزشی با Apache Spark آشنا شده و قدم به قدم آن را یاد می گیرید. سپس مدرس تحلیل و آنالیز بیگ دیتا بوسیله آن را به شما عزیزان یاد میدهد. 

عنوان اصلی : Apache Spark Hands on Specialization for Big Data Analytics

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Udemy است که بر روی 1 حلقه دیسک ارائه شده و به مدت زمان 9 ساعت و 53 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :


Introduction :
Breaking the Ice with Warm Welcome!
Course's Curriculum - Journey to the excellence!
Section 1 - Apache Spark Introduction and Architecture Deep Dive (being updated)

Apache Spark in the context of Hadoop Evolution :
Say Hello to Apache Spark - Thorough Dissemination of Capabilities
In-Depth Understanding of Spark's Ecosystem of High Level Libraries
Apache Spark and its integration within Enterprise Lambda Architecture
Apache Spark and where it fits in whole Hadoop Ecosystem

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
3 questions

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

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 1 حلقه دیسک
مدت زمان آموزش 9 ساعت و 53 دقیقه !
محصول موسسه آموزشی Udemy