Big Data Complete Course

Big Data Complete Course, available at $44.99, has an average rating of 3.5, with 36 lectures, based on 120 reviews, and has 27769 subscribers.

You will learn about Big Data Big Data Enabling Technologies Hadoop Stack for Big Data Hadoop Distributed File System (HDFS) Hadoop MapReduce with Example Spark Parallel Programming with Spark Spark Built-in Libraries Data Placement Strategies CAP Theorem Design of Zookeeper CQL (Cassandra Query Language) Spark Streaming and Sliding Window Analytics Kafka Machine Learning Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics Decision Trees for Big Data Analytics Predictive Analytics Spark GraphX & Graph Analytics This course is ideal for individuals who are Graduates or Software engineers or Developers It is particularly useful for Graduates or Software engineers or Developers.

Enroll now: Big Data Complete Course

Summary

Title: Big Data Complete Course

Price: $44.99

Average Rating: 3.5

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: ?7,900

Quality Status: approved

Status: Live

What You Will Learn

Big Data

Big Data Enabling Technologies

Hadoop Stack for Big Data

Hadoop Distributed File System (HDFS)

Hadoop MapReduce with Example

Spark

Parallel Programming with Spark

Spark Built-in Libraries

Data Placement Strategies

CAP Theorem

Design of Zookeeper

CQL (Cassandra Query Language)

Spark Streaming and Sliding Window Analytics

Kafka

Machine Learning

Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics

Decision Trees for Big Data Analytics

Predictive Analytics

Spark GraphX & Graph Analytics

Who Should Attend

Graduates

Software engineers

Developers

Target Audiences

Graduates

Software engineers

Developers

Big data is a combination of structured, semi structured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modelling and other advanced analytics applications.

Systems that process and store big data have become a common component of data management architectures in organizations, combined with tools that support big data analytics uses. Big data is often characterized by the three V’s:

the large volume of data in many environments;

the wide variety of data types frequently stored in big data systems; and

the velocity at which much of the data is generated, collected and processed.

Big datais a great quantity of diverse information that arrives in increasing volumes and with ever-higher velocity.

Big datacan be structured (often numeric, easily formatted and stored) or unstructured (more free-form, less quantifiable).

Nearly every department in a company can utilize findings from big data analysis but handling its clutter and noise can pose problems.

Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins.

Big data is most often stored in computer databases and is analysed using software specifically designed to handle large, complex data sets.

Topics Covered in these course are:

Big Data Enabling Technologies

Hadoop Stack for Big Data

Hadoop Distributed File System (HDFS)

Hadoop MapReduce

MapReduce Examples

Spark

Parallel Programming with Spark

Spark Built-in Libraries

Data Placement Strategies

Data Placement Strategies

Design of Zookeeper

CQL (Cassandra Query Language)

Design of HBase

Spark Streaming and Sliding Window Analytics

Kafka

Big Data Machine Learning

Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics

Parallel K-means using Map Reduce on Big Data Cluster Analysis

Decision Trees for Big Data Analytics

Big Data Predictive Analytics

PageRank Algorithm in Big Data

Spark GraphX & Graph Analytics

Case Studies of big companies and how they operate.

Course Curriculum

Chapter 1: Introduction to Big data

Lecture 1: Introduction

Lecture 2: Big Data Enabling Technologies

Lecture 3: Hadoop Stack for Big Data

Lecture 4: Hadoop Stack for Big Data Part 2

Chapter 2: Hadoop Distributed File System, MapReduce

Lecture 1: Hadoop Distributed File System (HDFS)

Lecture 2: Hadoop MapReduce 1.0

Lecture 3: Hadoop MapReduce 2.0

Lecture 4: Hadoop MapReduce 2.0 (Part-II)

Lecture 5: MapReduce Examples

Chapter 3: Spark, Parallel Programming with Spark, Built-in Libraries, Key-Value Stores

Lecture 1: Parallel Programming with Spark

Lecture 2: Introduction to Spark

Lecture 3: Spark Built-in Libraries

Lecture 4: Design of Key-Value Stores

Chapter 4: Data Placement Strategies, CAP Theorem, Zookeeper, CQL (Cassandra Query Language

Lecture 1: Data Placement Strategies

Lecture 2: CAP Theorem

Lecture 3: Consistency Solutions

Lecture 4: Design of Zookeeper

Lecture 5: Design of Zookeeper Part 2

Lecture 6: CQL (Cassandra Query Language)

Chapter 5: Design of HBase, Spark Streaming and Sliding Window Analytics , Kafka

Lecture 1: Design of HBase

Lecture 2: Spark Streaming and Sliding Window Analytics Part 1

Lecture 3: Spark Streaming and Sliding Window Analytics Part 2

Lecture 4: Sliding Window Analytics

Lecture 5: Kafka

Chapter 6: Big Data Machine Learning

Lecture 1: Big Data Machine Learning

Lecture 2: Big Data Machine Learning Part 2

Lecture 3: Machine Learning Algorithm K-means using Map Reduce for Big Data Analytics

Lecture 4: Parallel K-means using Map Reduce on Big Data Cluster Analysis

Chapter 7: Big Data Analytics

Lecture 1: Decision Trees for Big Data Analytics

Lecture 2: Big Data Predictive Analytics

Lecture 3: Big Data Predictive Analytics Part 2

Chapter 8: Case Study, PageRank Algorithm , Spark GraphX & Graph Analytics

Lecture 1: Parameter Servers

Lecture 2: PageRank Algorithm in Big Data

Lecture 3: Spark GraphX & Graph Analytics

Lecture 4: Spark GraphX & Graph Analytics Part 2

Lecture 5: Case Study: Flight Data Analysis using Spark GraphX

Instructors

Edcorner Learning
Be Incredible

Rating Distribution

1 stars: 6 votes

2 stars: 7 votes

3 stars: 23 votes

4 stars: 33 votes

5 stars: 51 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!