Big Data - Terms and definition

What is Big Data?
Big Data refers to large sets of data that cannot be analyzed with traditional tools. It stands for data
related to large-scale processing architectures. Big Data refers to the data sets whose size makes it difficult for commonly used data capturing software tools to interpret, manage, and process them within a reasonable time frame.

What is Hadoop?
Hadoop is the software framework that is developed by Apache to support distributed processing of
data. Hadoop is an open-source framework built on the Java environment. It assists in the
processing of large data sets in a distributed computing environment.

MapReduce
The MapReduce is a core component of Hadoop, and is responsible for processing jobs in
distributed mode.

HDFS
The Hadoop Distributed File System (HDFS) is a distributed file system that shares some of
the features of other distributed file systems. It is used for storing and retrieving
unstructured data.

NameNode and DataNode
Hadoop is a master and slave architecture that includes the NameNode as the master and the
DataNode as the slave.

Pig
The Apache Pig is a platform which helps to analyze large datasets that includes high-level
language required to express data analysis programs. Pig is one of the components of the
Hadoop eco-system.

Hive
Hive is an open-source data warehousing system used to analyze a large amount of dataset
that is stored in Hadoop files. It has three key functions like summarization of data, query,
and analysis.

HBase
It is a distributed, column-oriented database built on top of HDFS (Hadoop Distributed
Filesystem). HBase can scale horizontally to thousands of commodity servers and petabytes
by indexing the storage.

ZooKeeper
It is used for performing region assignment. ZooKeeper is a centralized management service
for maintaining and configuring information, naming, providing distributed synchronization,
and group services.

Cloudera
It is a commercial tool for deploying Hadoop in an enterprise setup.

Sqoop
It is a tool that extracts data derived from non-Hadoop sources and formats them such that
the data can be used by Hadoop later

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