Data science is an interdisciplinary sphere of study that has gained traction over the years, given the sheer amount of data we produce on a daily basis — projected to be over 2.5 quintillion bytes of data. The field of study utilizes modern techniques and tools to extract useful information, find undiscovered patterns and make business resolutions from structured and unstructured data. As data science utilizes both structured and unstructured data, the data used for analysis can be obtained from an array of application domains and available in multiple formats.

Data science tools are used to construct predictive models and generate meaningful information by extracting, processing, and analyzing structured and unstructured data. The primary goal of implementing data science software is to clean up and standardize data by uniting machine learning (ML), data analysis, statistics, and business intelligence (BI).

With data science tools and techniques, you can easily determine the main cause of a problem by addressing the right questions, explore and study data, model various forms of data using algorithms and visualize and communicate results via graphs, dashboards, etc. Data science software like Apache Hadoop comes with pre-defined functions, a suite of tools and libraries.

Apache Hadoop is one of the few data science tools you should have in your kit. 

What is Hadoop?

Apache Hadoop is an open source software designed for reliable, distributed and scalable computing. The Hadoop software library is designed to scale to thousands of servers, each of which offers local computation and storage.  

Using simple programming models, you can process large sets of data across computer clusters in a distributed manner. The software solves complex computational tasks and data-intensive problems by dissecting large sets of data into chunks and sending them to computers with instructions. 

The Hadoop library does not depend on hardware resources to deliver high availability; all failures are detected and rectified at the application layer. 

Understanding the Apache Hadoop Ecosystem

The Hadoop ecosystem consists of the following major components:

• HDFS (Hadoop Distributed File System) – HDFS is the backbone of the Hadoop ecosystem that is responsible for storing large sets of structured, semi-structured and unstructured data. With HDFS, you can store data across various nodes and maintain metadata. 

HDFS has two core components, NameNode and DataNode. A NameNode executes operations like opening, closing and changing the names of files and directories and the mapping of blocks to DataNodes. 

DataNodes on the other hand serve read and write requests from clients and perform block creation, deletion and replication upon instruction.  

• YARN (Yet Another Resource Negotiator) – YARN performs all processing activities by scheduling tasks and allocating resources and is essentially the brain of the Hadoop ecosystem. It consists of two primary components, ResourceManager and NodeManager. 

Resource Manager processes requests, divides large tasks into smaller tasks, and assigns these tasks to NodeManagers. NodeManagers are responsible for the execution of tasks on DataNodes. 

• MapReduce – MapReduce is the primary component of processing in the Hadoop ecosystem, based on the YARN framework. It contains two main functions, Map() and Reduce ().

Map() performs functions like sorting, grouping and filtering on data received as input to produce Tuples (key-value pairs). Reduce () aggregates and summarizes the Tuples produced by Map(). 

• Apache PIG and Apache HIVE – Apache PIG works on the Pig Latin language, a query-based language that is similar to SQL. PIG is a platform for data structuring, processing and analyzing. Apache HIVE uses an SQL-like query language called HQL to read, write and manage large sets of data in a distributed environment.
• Apache Spark – Apache Spark is a framework written in Scala that handles all process consumptive jobs like iterative or interactive real-time processing, batch processing, visualization and graph conversions.
• Apache Mahout – Apache Mahout offers an environment for creating scalable ML applications. Its functions include frequent item set missing checks, classification, clustering and collaborative filtering. 
• Apache HBase – Apache HBase is an open source NoSQL database that supports all types of data and is modeled after Google’s BigTable, thereby enabling effective handling of Big Data data sets.  
• Hadoop Common – Hadoop Common provides standard libraries and functions that support Hadoop ecosystem modules.   
• Other components of the Hadoop ecosystem include Apache Drill, Apache Zookeeper, Apache Oozie, Apache Flume, Apache Sqoop, Apache Ambari and Apache Hadoop Ozone. 

Understanding Apache Hadoop Architecture

The Apache Hadoop framework consists of three major components:

• HDFS – HDFS follows a master/slave architecture. Each HDFS cluster has a solitary NameNode that serves as a master server and a number of serving DataNodes (usually one per node in the cluster). 
• YARN – YARN performs job scheduling and resource management. ResourceManager receives jobs, divides jobs into smaller jobs and assigns these jobs to various slaves (DataNodes) in a Hadoop cluster. These slaves are managed by NodeManagers. NodeManagers ensure the execution of jobs on DataNodes. 
• MapReduce – MapReduce works on the YARN framework and performs distributed processing in parallel in a Hadoop cluster. 

Using Apache Hadoop

Apache Hadoop is a must-have data science tool. It offers all the provisions you need to process large sets of data. You can avail of the software for free and use its capabilities to create a tailored solution for your enterprise. Explore the Hadoop ecosystem in detail and learn about each of its components to build a solution of promise.

Read next: Data Management with AI: Making Big Data Manageable

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