Course Description
Welcome to our course. Looking to learn Apache Spark 2.0, practice end-to-end projects and take it to a job interview? You have come to the RIGHT course! This
course teaches you Apache Spark 2.0 with Java, trains you in building
Spark Analytics and machine learning programs and helps you practice hands-on (2K LOC code samples !) with an end-to-end real life application project. Our goal is to help you and everyone learn, so we keep our prices low and affordable.
Java is the main technology used today to build industry-grade
applications and coming that with Spark gives you unlimited ability to
build cutting edge applications.
Apache Spark is the hottest Big Data skill today. More and more
organizations are adapting Apache Spark for building their big data
processing and analytics applications and the demand for Apache Spark
professionals is sky rocketing. Learning Apache Spark is a great vehicle
to good jobs, better quality of work and the best remuneration packages.
The goal of this project is provide hands-on training that applies directly to real world Big Data projects. It uses the learn-train-practice-apply methodology where you
- Learn solid fundamentals of the domain
- See demos, train and execute solid examples
- Practice hands-on and validate it with solutions provided
- Apply knowledge you acquired in an end-to-end real life project
Taught by an expert in the field, you will also get prompt response to your queries and excellent support from Udemy.
Course Details
Your learning Process - Learn, Train, Practice and Apply@Practice() : Key-Value RDDs
@Practice() Actions
@Practice() Advanced Spark
@Practice() Loading and Storing Data
@Practice() SQL Data Frames
@Practice() Temp Tables/ Views
@Practice() Transformations
<train/> Actions
<train/> Advanced Spark - Enhanced Capabilities
<train/> Decision Trees Use Case
<train/> Key-Value RDDs
<train/> K-Means Clustering Use Case
<train/> Linear Regression Use Case
<train/> Loading and Storing Data
<train/> Naive Bayes and Text Pre-processing Use Case
<train/> Random Forests and PCA Use Case
<train/> Recommendations Engines Use Case
<train/> Run your First Spark / Java program
<train/> Setup your Spark/Java environment
<train/> Spark Streaming
<train/> SQL Data Frames
<train/> Temp Tables / Views
<train/> Transformations
Actions - Extract insights from Data
Advanced Spark
Analyzing Results and Errors
Apache Spark eco-system
BONUS Lecture : Other courses you should check out
Closing Remarks
Connecting to Spark on a different Server
Decision Trees Classification
Final Solution Review - we did it !
Hints to help you with the project
Key-Value RDDs
K-Means Clustering - grouping similar items
Linear Regression - fit to a line
Loading and Storing Data
Meet Your Mentor
Naive Bayes Classification
Principal Component Analysis
Random Forests Classification
RDD : The foundation of Spark
Real world problem statement - Credit Card Defaulters
Recommendation Engines
Resource Bundle
Section 1: Kick-start your learning
Section 2: Introduction to Apache Spark
Section 3: Spark Programming with Java
Section 4: Spark SQL
Section 5: Spark Streaming
Section 6: Machine Learning with Spark
Section 7: APPLY - Your Course Challenge Project
Section 8: Conclusion
Spark Architecture
Spark Architecture - How it all works
Spark ML Concepts - new data types
Spark Project work flow - How it gets done
Spark SQL Data Frames - the new era
Spark Streaming - real time data processing
Spark Streaming Architecture - How it works
Start your Spark Engines
Temp Tables /Views - Easy querying
Text Pre-processing with TF-IDF
Transformations - Change how data looks
Types of Analytics - Simple to Predictive
Types of Machine Learning
Your Course Guide - Pathway to success
LINK FOR THE FREE COURSE
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