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
Apache Hive has many exciting features. Let's discuss them one by one:
Hive provides data summarization, analysis, and query in a much more relaxed manner.
Hive supports different external tables which make it possible to analyze data without actually storing in Hadoop File System.
Apache Hive fits perfectly with Hadoop's low-level interface specification.
It also supports the partitioning of data at the level of tables to improve performance.
Hive has a rule-based optimizer to optimize logical plans.
It is familiar, scalable, and extensible.
Using HiveQL doesn't require any expertise of programming language, Knowledge of basic SQL queries is enough.
We can efficiently process structured data in Hadoop using Hive.
Querying in Hive is very straightforward as it is a SQL like a language.
We can also run Ad-hoc data analysis queries using Hive.
Don't miss out!