What Is Big Data Hadoop? (Definition And Career Opportunities)

Organisations collect user information known as big data and use Hadoop to organise, store and analyse it. It helps understand customers better and facilitates product improvement, innovation and drive business decisions. Learning about this software may help you advance your career in the IT sector. In this article, we will discuss what is big data Hadoop, its advantages and limitations in managing data and job opportunities in this industry.

What is big data Hadoop?

Big data refers to large sets of complex data collected in real time from different data sources. The volume of data is large, making it difficult to process using traditional data processing software. Hadoop is an open-source framework designed to store and analyse various types of data. It handles structured, semi-structured and unstructured data.

The use of Hadoop makes it easy to work with big data. It makes the process of data storage and management economical and scalable for organisations to implement. Hadoop is an ecosystem of libraries. It does not depend on the hardware to produce results, but each of its libraries performs a specific task to deliver answer queries. Its design can be scaled from one server to thousands of machines because of its horizontal scaling algorithm.

Characteristics: 5 Vs of big data

Learning about the characteristics of big data may help understand its concept better. These characteristics have evolved with big data itself. The five main traits of big data are:

1. volume: the amount of data generated is extensive

2. velocity: a continuous flow of data at massive rates

3. variety: structured, semi-structured and unstructured data collected simultaneously from heterogeneous sources

4. value: evaluating the use of the bulk of data collected for business

5. veracity: data collected from disparate sources checked for its accuracy and quality

Where is big data used?

To effectively make use of a large amount of information, businesses develop a big data strategy. The primary goal of analysing big data is to identify user behaviour and patterns using tools like machine learning, predictive modelling, data mining and statistical analysis. It is popular in several industries like medicine, agriculture, manufacturing and retail. Some popular applications of big data include:

• storing real-time stock exchange data to track buyer and seller actions

• analyse market data and conduct risk management in financial services

• streamline supply chain management, optimise delivery routes and predict the location-based requirement

• record black box data of the crew on helicopters, aeroplanes and jets and performance of the aircraft

• record patient data, provide easy access to reports and track patient progress

• track user behaviour on the internet to understand their content consumption

• track user behaviour on e-commerce websites to analyse product preference, purchase patterns and enable re-targeting promotions

• improve the interaction between government and citizens, facilitate better decision making and automate routine government processes

• track landscapes to identify energy consumption patterns, prevent crime, predict natural disasters and enhance resource management

Related: 18 Big Data Examples (Common Uses in Different Industries)

Benefits of big data

Companies that collect a large amount of data, and take measures to access and analyse it, may produce results that benefit all stakeholders. Essentially, big data facilitates research that helps make better business decisions. Some advantages of using big data in business include:

• helps understand user behaviour to provide customised services and improve user experience

• identifies user pain points and facilitates innovation of new products or improving existing ones

• mitigates risks with predictive maintenance

• facilitates online reputation management with sentiment analysis

• automates routine processes to improve operational efficiency

• analyses results of digital campaigns, promotions and identify ways to improve it using the available user information

Challenges with big data

All information collected can deem useless if it is not organised and used efficiently. If companies do not have the necessary tools and expertise to deal with big data, it may pose several challenges. Some of the common issues with handling big data include:

• the large amount of data collected requires extensive infrastructure for storage and management

• the variety of data collected as audio, video, text, images and documents simultaneously makes processing difficult

• overload of useless information may hinder analysis

• infrastructure to ensure privacy and security of data is essential

• Transferring data is cumbersome and time-consuming. It exponentially increases the time spent on analysing and processing it.

Advantages of using big data Hadoop

Hadoop is open-source software built on Java. It makes data accessible for modification to suit the business requirement. It is designed to process large amounts of data sets and works on an ecosystem approach to acquire, arrange, process and analyse data. Using Hadoop for big data is advantageous in several ways:

Storage and processing of data

Big data Hadoop uses Hadoop Distributed File System (HDFS) to break data into clusters. The DataNodes store the information, while the NameNodes contain metadata of the values stored on nodes. Additionally, it uses Yet Another Resource Negotiator (YARN) to manage and schedule data. The central node or the resource manager interacts with the nodes to process all queries and requests.

Related: All You Need To Know About Data Warehouse Architecture

Retrieves data quickly

Hadoop uses MapReduce, which includes functions Map() and Reduce() to filter, sort and return the desired output. Instead of handling data sequentially, MapReduce splits and runs concurrently across the servers. It helps reduce the time required to process a query. Other Hadoop projects, like Pig and Hive, help improve data processing speeds using parallel computation for ad hoc querying.

Scalability

Hadoop follows horizontal scaling that allows storing data in a distributed environment. It also requires minimal hardware configurations to set up. Some organisations may also deploy it to the cloud. The data stored in clusters are easy to process, retrieve and simplify scalability.

Fault tolerance

Hadoop follows a distributed ecosystem. It breaks each block of data and stores it on different nodes. The information on one node gets replicated on other nodes of the cluster. This way, if the node does not function properly, the values get extracted from the other nodes.

Stores various data types

The HDFS is capable of storing structured, semi-structured and unstructured data. It could include documents, audio, video, images and text. It does not categorise or validate the data during storage. Instead, it gets analysed and fits a schema when retrieving data.

Challenges with using big data Hadoop

Hadoop is a widely popular open-source framework. It makes data processing easier than the traditional methods. Although, it has some limitations and challenges:

• complex software that requires time and effort to learn

• experience in Java, Python or C++ is necessary

• performing iterative and interactive analytical tasks is not efficient

• uses parallel computation and distributed ecosystems, which process data slowly

• data is not encrypted

Job opportunities

Big data Hadoop requires professional skills. It requires developers, analysts and administrators to ensure smooth working. Some popular job titles in this field include:

1. Data analyst

National average salary: ₹4,48,844 per year

Primary duties: Data analysts work in organisations to analyse raw data and derive insights that can help in making better business decisions. They remove coding errors and corrupted data. Data analysts use several automated and statistical tools to study big data.

Related: How To Become a Data Analyst: A Complete Guide

2. Data administrator

National average salary: ₹8,39,024 per year

Primary duties: Data administrators are responsible for organising data. They supervise database software modifications, ensure the data meets the compliance policies and is securely stored. Additionally, they also evaluate new software that can help maintain data better.

3. Data scientist

National average salary: ₹8,50,138 per year

Primary duties: Data scientists are knowledgeable in mathematics, statistics and computer science. They manage data, solve complex problems and provide organisations with deep insights. It helps the management and developers create better products and make better business decisions.

Related: What Does a Data Scientist Do? And How To Become One

4. Hadoop developer

National average salary: ₹9,07,263 per year

Primary duties: A Hadoop developer understands the requirements of the organisation and develops Hadoop applications. They are responsible for analysing the results and modifying the code if required. Furthermore, they create data processing frameworks for the organisation to manage its big data.

5. Data engineer

National average salary: ₹10,58,061 per year

Primary duties: Data engineers have strong technical knowledge in coding and database design. They understand the business objectives and build algorithms to utilise raw data efficiently. They optimise the process to access data, develop dashboards and visualisations for better understanding.

Salary figures show data listed on Indeed Salaries at the time of writing the article. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.

Please note that none of the companies mentioned in this article are affiliated with Indeed.