Hadoop – All Core Elements You Need To Know

Apache Hadoop today is the principal force that drives our Big Data market. It was Cloudera’s Chief Architect, Dough Cutting who assisted in developing Apache Hadoop to address the enormous data explosion. Initially influenced by the published Google papers, that presented a useful approach to manage excess data, today Hadoop has become the standard medium or mechanism for processing, storing and evaluating terabytes and petabytes of data. It’s the latest buzzword in the IT landscape that includes other allied technologies like Pig, Hive, Flume, Zookeeper and Oozie.

Defining Apache Hadoop

Apache Hadoop can be defined as a 100% open source software that presents a revolutionary way to process and store bulk data. Rather than depending on costly, proprietary hardware and various systems to process and store data, Apache Hadoop facilitates distributed parallel processing of colossal data across cost-effective and standard-industry servers that enables companies to store, process and scale data without any restrictions.

There is no data that is too big or excess for Hadoop. Furthermore, in today’s interconnected and hi-tech era where data output is increasing with every passing day, Hadoop brings forth innovative benefits for organizations and business houses, that makes it possible for them to attach value to data that previously was considered ineffective (also consider checking out this perfect parcel of information for data science degree).

The Apache Hadoop Ecosystem

Simply put, the Hadoop ecosystem or network comprises of HDFS (Hadoop Distributed File System), a distributed and scalable storage structure working very close with MapReduce. On the other hand, MapReduce is a programming structure and an allied execution for generating and processing huge data sets.

Hadoop Components & their Functions

Operating all by itself, Hadoop can’t work wonders. It consists of a set of core components like Hive, PIG, MapR, Sqoop and HBase, that results in excellent performance. Let’s have a look at all the components and their functions.

PIG – This platform is utilized to control data stored in HDFS. It includes a compiler for MapReduce programs along with a high-end language PIG Latin.

MapReduce – It’s a software program aimed to process big data sets and is a game-changer in the domain of big data processing. A huge data process might require 20 hours for processing on a centralized relational database structure. However, when distributed across a big Hadoop set of commodity servers the same process can be completed in 3 minutes with parallel processing.

Sqoop - Simply put, it’s a command line interface application that transmits data between the relational databases and Apache Hadoop.

Hive - It is a SQL type and data warehousing query language that represents data in a table format. Hive further supports Lists, Associative Arrays, Structs, de-serialized and serialized API’s used to shift the data in and out of the tables. Hive also comprises of a series of data models.

Oozie – A workflow scheduler system, Oozie monitors Hadoop jobs. Being a server-oriented Workflow Engine it specializes in functioning workflow tasks with the activities that operate on Hadoop PIG and MapReduce jobs. It is executed as a Java-Web Application and it functions within a Java Servlet-Container.

HBase – This is a Hadoop Database and can be described as a huge, scalable and distributed data store. Being a sub-project of Apache Hadoop Project it offers real-time reading and writing access to Big Data.

YARN- A core component of the second generation Hadoop ecosystem, it was initially outlined by Apache as a re-modelled resource manager. However, today it gets characterized as a huge-scale, distributed OS for Big Data applications.

Hadoop & other Miscellaneous Applications

Hadoop has created a benchmark for itself in terms of its excellence and performance. Today, Hadoop is widely used across multiple fields that provide positive results. Its combination with various applications is beneficial, regardless of whether it is combined with applications like SAP HANA, Cassandra, MongoDB or Apache Spark (also consider checking out this career guide for data science jobs).

The All New Hadoop 2.0

Hadoop 2.0 is an attempt to design a new structure aimed at Big Data processing, storing and data mining. It enables the generation of new data technologies in Hadoop that were absent earlier owing to architectural restrictions.

Today, candidates and professionals with a Hadoop training and certification have the chance of bagging enterprising career opportunities.