i
Characteristics of Big Data
Application of Big Data Processing
Introduction to BIG DATA
Where to get Big Data?
Types of Big Data
Storage layer - HDFS (Hadoop Distributed File System)
MapReduce
YARN
How Hadoop works?
Hadoop Eco System
Hadoop Architecture
Hadoop Installation & Environment Setup
Setting Up A Single Node Hadoop Cluster
Ubuntu User Configuration
SSH Setup With Key Generation
Disable IPv6
Download and Install Hadoop 3.1.2
Working with Configuration Files
Start The Hadoop instances
Hadoop Distributed File System (HDFS)
HDFS Features and Goals
HDFS Architecture
Read Operations in HDFS
Write Operations In HDFS
HDFS Operations
YARN
YARN Features
YARN Architecture
Resource Manager
Node Manager
Application Master
Container
Application Workflow in Hadoop YARN
Hadoop MapReduce
How MapReduce Works?
MapReduce Examples with Python
Running The MapReduce Program & Storing The Data File To HDFS
Create A Python Script
Hadoop Environment Setup
Execute The Script
Apache Hive Definition
Why Apache Hive?
Features Of Apache Hive
Hive Architecture
Hive Metastore
Hive Query Language
SQL vs Hive
Hive Installation
Apache Pig Definition
MapReduce vs. Apache Pig vs. Hive
Apache Pig Architecture
Installation Process Of Apache Pig
Execute Apache Pig Script
Hadoop Eco Components
NoSQL Data Management
Apache Hbase
Apache Cassandra
Mongodb
Introduction To Kafka
The Architecture of Apache Flume
Apache Spark Ecosystem
Enterprises can use external intelligence when making decisions: Using the information stored on the social network, such as Facebook, marketing agencies learn how to respond to their campaigns, promotions, and other advertising media. Their manufacturing is planned by using social media data such as preferences and brand perception of their customers, product businesses, and retail organizations.
Enterprises can offer improved service to customers: New systems designed with Big Data technologies are replacing traditional customer feedback systems. Big data and natural language processing (NLP) technologies are used for reading and evaluating consumer responses in these new systems.
Better operational efficiency: Big Data techniques can be used before defining which information should be transferred to the data warehouse to create a staging area or landing zone for new information. Furthermore, such integration of Big Data technologies and data warehouse helps an organization to discharge rarely accessed data. An excellent example of this is Health care data. Using patients’ history data, hospitals are providing better and quick service.
Don't miss out!