Big Data Complete Course | Udemy Free Download

Big Data Complete Course | Udemy
English | Size: 15.62 GB
Genre: eLearning

What you’ll 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

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

Big data can 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

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.

Who this course is for:
Graduates
Software engineers
Developers

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