Best Big Data Analytics Tools in 2024 for Handling Massive Amount of Data

It is the age of data and evolution of information through sharing, communication, and analysis. By bridging the gap between geographies and thoughts with the help of the Internet of things, we are moving towards a data-driven future.

Working in this direction, the role of data synthesis and analysis has become a crucial aspect. Data is gathered from various sources and synthesised to form conclusive observations, which can then be recorded as studies and reports. However, to make the optimal use of data sets, you require specialised tools. This is where big data analytical tools come into play.

What is Big Data Analytics?

Big data analytics is the compilation, observation, and reporting of varied data clusters, known as big data, to uncover information. Big data, with the help of analytical tools, is used by businesses to understand correlations, trends, and preferences and make informed decisions.

High velocity, volume, and veracity are the three aspects that make big data. These information clusters provide innovative and cost-effective forms of analytical findings so that businesses can enhance their decisions and automate their processes.

Industries Which Have Been Revolutionized by Big Data

Almost every industry uses some form of big data technology to optimize their processes. Here are some of the sectors that have been revolutionised by big data:

• Healthcare

Healthcare is one of the biggest recipients of the benefits of Big Data. Modern healthcare depends on relevant data and analysis to come to conclusions and take necessary steps. Specialists use big data analytics to track symptoms and signs of a disease, and create treatment modules.

Moreover, big data also assists healthcare professionals in managing patient and institutional data, improving diagnoses, patient treatment, and report creation.

• Retail

Big Data Analytics have revolutionized the global retail market within a small period. In fact, according to a report, big data analytics was estimated in the retail market at $4.18 billion in 2019.  This shows that it is essential for a retailer to utilize big data analytics to understand the requirements of customers.

With the valuable insights gained through big data analytics tools, retail companies can work upon improving their products, services, and overall enhancement of their business. Analytic reports help retailers customise their products and services and strategize better returns.

• Transportation

Like the healthcare and retail industry, the transportation industry too relies heavily on big data and analytics. This the analytical reports generated with the help of big data analytics are used to enhance services and provide a smooth transit.

With the help of analytics, transportation businesses can forecast the weather, traffic, ETA and much more, to map out the travelling experience.

• Hospitality

Big data is revolutionizing the hospitality industry by understanding current consumer requirements, guest preferences, booking analysis, and other metrices such as purchase patters and active booking period.

With the help of big data analytics, hospitality marketing professionals can recommend personalised options that suit to consumers choices and provide offers that the guests would prefer.

• Banking

The risks in banking industry are high, from fraud to credit risk, it takes a lot of logistical thought into making transactions safe. Big data analytics can predict weak points in the security to prevent security breach such as fraud, card and cheque fraud detection, currency duplicity and much more.

It also helps bank managers and owners to predict various investment opportunities to help the brank grow.

10 Best Big Data Tools of 2024 for Businesses Analytics

• Apache Spark
• MongoDB
• Pentaho
• Apache Hadoop
• Cassandra
• OpenRefine
• RapidMiner
• Quoble Data
• Elastic Search
• Tableau

• Apache Spark

Apache Spark is a one-of-its-kind cluster computing big data software that offers multi-level APIs in various languages such as Scala, Java, R, and Scala, Python. Its engine is customised and provides various essential execution graphs to help understand data analytics.

It is feature rich and comes with various tools including MLlib to be used in machine learning, Spark SQL for structured processing of data, Graphx which will help in graph processing, and much more.

• MongoDB

Mongo DB is one of the top big data tools available in the market, that offers cross-platform features for indexing and querying. It helps produce analytical reports with optimal performance, availability, and scalability.

One of the best aspects of MongoDb is that it is open source. Named the best DMBS system of 2019 by DB engines, it offers a NoSQL database that is ideal for processing big data. To find more similar options look into the best MongoDB alternatives.

• Pentaho

Pentaho’s big data analytics offer a range of tools to collect, synthesise, and generate visualised reports. It is ideal for businesses that wish to monitor various marketing and organisational insights.

It enables businesses to create a framework with the help of big data tools and visually represent them with the help of graphs and charts for accurate insights.

• Apache Hadoop

Apache Hadoop big data software library is an analytics architecture that helps businesses and organisations compile scattered data clusters with the help of standard programming tools.

This big data tool is designed to optimally use single server as well as multiple data cluster machines, Hadoop offers state-of-the-art big data cloud computing ideal for growing businesses.

• Cassandra

Apache Cassandra DBMS is a lightweight and advanced big data analytics solutions that provides scalable analytics reports. Its multiple graphical format and fault-tolerance on cloud and hardware infrastructure make it an ideal big data platform for businesses that deal with critical data.

Cassandra’s analytics help in data evolution across multiple data machines, and decreases latency with its fail-safe model for the users to prevent regional outages.

• OpenRefine

As the name suggests, OpenRefine is an open-source analytics tool used for big data analytics and reporting. With the help of OpenRefine, businesses can easily extract crucial data amongst the vast data clusters to provide innovative insights.

In addition, it also provides the ability to integrate the processes with data from third party sources and web services. One of its best features is that it supports a wide range of data formats to make the most out of the data reservoir and provide accurate insights.

• RapidMiner

RapidMiner is another top big data software platform that helps businesses predict logistical reports of various business metrices. The big data technologies provide an integrated ecosystem for machine learning, data compilation, deep learning, data mining, and predictive analytics. As the name suggests it is ideal for businesses that are looking for quick text and data mining solutions.

• Qubole Data

Qubole Data is an autonomous big data analytics platform that is ideal for all kinds of businesses looking to manage and optimise their data clusters to focus on business insights and strategies.

One of its best features is that all the analytical tasks can be executed through a single platform. It comes with open-source engines that have been customised for cloud computing and are ideal for monitoring, compliance and all-round security.

• Elasticsearch

Elasticsearch is another optimally scalable big data and analytics tool that comes with an advanced open-source text mining engine. It offers businesses the ability to mine, synthesise and compile large clusters of data in a short period.

It comes with the ability to provide specific information to data driven applications with the help of its schema-free documents and HTTP web interface.

• Tableau

Tableau big data software enables businesses to seamlessly process and receive data insights from unsorted data clusters. It can be integrated with Hadoop to receive datasets and recognise queries essential for the business.

They provide easy to understand graphs and visual charts for an in-depth understanding of vital insights related to the business. With Tableau, business owners and data managers can design a comprehensive data-oriented infrastructure to map profound understanding of logistics.

Big Data: Applications & Benefits for Growing Businesses

The benefits of implementing big data for the overall growth of a business are plenty. Here are some reasons why businesses are using Big Data:

• Big Data for Cutting Business Expenses

One of the biggest benefits of using big data analytical tools in businesses is that it helps in cutting expenses. Big data also helps businesses cut overhead expenses of the entire supply-chain by providing optimum budget for each stage, such as procurement, production, packaging, transportation and distribution.

•  Big Data for Increased Efficiency

For instance, a telecom channel uses Google Earth to perform an analysis of the causes of low connectivity and call dropping of a particular area. Similarly, a bank that deals with mortgages can execute risk assessment of a new case by comparing foreclosure and the default data provided by Google Maps.

• Big Data for Market Analysis

With the help of big data analytics tools, businesses can get a better understanding of the current marketing trends, consumer demands and strategize accordingly.

For instance, hike in demand of a product at a certain time of the day or month can help businesses advertise their product accordingly. This can result in increases sales and finally, more profits.

• Big Data for Studying Competition

Businesses can also study the marketing techniques of their competitors to come up with better strategies to steer the market towards their brand. Big Data analytics tools help in gathering periodic performance data of your competitors.

It also helps in analysing marketing campaigns for valuable insights of what works and what doesn’t.

•  Big Data for Customer Loyalty

With the growing digital awareness, the shopping trends have transformed. A big part of consumer-base today likes to purchase products and services online.

Therefore, the purchase patterns of an internet user can help businesses sell, upsell and cross-sell products that might be relevant to the customer in question.

Big data and analytics help businesses tailor their products and services to suit the customer’s choices. With customer’s digital footprint businesses can pitch their products in the form of targeted advertisements while they browse online social media channels.

• Big Data for Employee Management

Another way companies can utilize big data analytics, is by applying its capability for employee management and hiring. With the help of keyword clusters and filters, businesses can skim the profiles of candidates that suit the job profile.

This eliminates the hassles of paying a third-party recruiting agency and directly receive leads.

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