What is Hadoop?

Hadoop is an open-source framework for data processing based on the Hadoop Distributed File System (HDFS). The creator of Apache Lucene, developed this framework.

It has since become a top-level project within the Apache Software Foundation, responsible for developing it further ever since its acquisition in 2008.

HDFS

 

HDFS: The Hadoop distributed file system (HDFS) is used to provide scalably fault-tolerant shared storage across clusters of computers to hold massive amounts of data.

it can be used with any data that stacks well in tabular form.

This includes business data such as customer transactions, which are stacked horizontally or “column-oriented” tables; web pages stored in a convenient format for Hadoop’s MapReduce programming model.

And emails, which can be processed as they arrive without storing them in a database.

The Hadoop Distributed File System comprises nodes, each of which stores part of the overall data.

The nodes are connected using the Hadoop Distributed File System NameNode, the master server.

The Hadoop Distributed File System is designed to scale out to thousands of nodes, ideal for storing and processing large datasets.

Hadoop also includes several data analysis and batch processing utilities, such as Pig and Hive.

These allow developers to write code that processes data in Hadoop, which is then made available for analysis in a form where data can be easily queried and processed.

Therefore, provides a framework for writing distributed data processing software, allowing you to store huge amounts of unstructured data, perform online analytics on it without changing the way you process your own data or integrate that with other applications.

In addition, Hadoop includes HBase, a non-relational database written for MapReduce programming model. What Is Big Data?

HBase stores a large amount of structured data in a column-oriented structure, similar to Google’s BigTable.

Hadoop MapReduce:

MapReduce” is a software platform and computational model used for creating applications that run on Hadoop.

MapReduce software can process huge amounts of data simultaneously across large computational nodes in large clusters.

Features

Suitable for Big Data Analysis

The cluster will be your most suitable option to analyze Big Data due to its unstructured and distributed nature.

Since all processing and algorithm (not the data) is transferred to computing nodes, less bandwidth is used.

A concept known as data locality can help improve Hadoop-based applications’ efficiency. Apply: What Is Big Data Analytics?

Scalability

HADOO clusters can easily scale up by adding additional nodes, allowing Big Data to grow.

Additionally, scaling does not require modifying application logic.

Fault Tolerance

HADOOP ecosystem allows replication of input data to other cluster nodes.

In the event of a cluster node failure, data processing can still be performed using data stored on another cluster node.