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Top 10 Big Data Analytics Tools Used by Tech Giants In 2022

One of the most commonly used terms in technology is “big data.” Big Data Analytics holds a lot of promise, given the volume of data created every minute by customers and enterprises all over the world. As a result, organizations must manage, store, visualise, and analyze massive amounts of usable data. Because typical data tools aren’t designed to handle this degree of complexity and volume, a slew of specialist Big Data software tools and architectural solutions have sprung up to help.

Businesses may utilze custom-built Big Data Tools to put their data to work, discover new opportunities, and start new businesses. These solutions, unlike traditional Big Data technology, go one step further by bringing context and meaning to the raw data. Rather than being just a repository for individual records, Big Data Analytics Tools allow businesses to view the bigger picture that data may bring.

This blog will provide an overview of Big Data as well as the need for Big Data tools and technologies. Furthermore, this article will assist you in learning about the robust Big Data Tools that will be most popular among IT giants in 2022. So, let’s get started.

Concept Art and Visualization of “Big Data” the most influence trend in Digital Era. Concept Inspiration from Big Bang Theory.

What is Big Data?

Big Data is a term that refers to large, diverse amounts of data that are rapidly rising. It is called “Big” not only because of its vastness but also because of its incredible diversity and complexity. Its ability to accumulate, organize, and process data often outperform traditional databases.

Big Data can originate from wherever on the earth that we can digitally monitor.

Need for Big Data Tools and Analytics

Big Data Tools are often used to extract and process information from a large number of data sources. The volume of data accessible isn’t the sole factor that determines the worth of Big Data. How you use it determines its value. The Big Data ecosystem is accelerating at an alarming rate. A diverse set of analytic approaches currently supports a wide variety of corporate operations.

  • Descriptive Analytics can help users figure out “what happened and why.” This sort of Analytics incorporates various query and reporting options, as well as scorecards and dashboards.
  • Predictive Analytics can be used by users to determine the likelihood of a given event occurring in the feature. Just a few instances are emergency alert systems, spam detection, inspection and maintenance applications, and forecasting.
  • Prescriptive Analytics provides specific (prescriptive) recommendations to the user. They answer the question, “What should I do if “x” happens?”
Blue graphs and icons.

When you combine Big Data with high-performance Analytics, you may easily fulfil business-related tasks. Processing a large amount of data in traditional databases can be difficult. As a consequence, you can effortlessly manage your data with Big Data Tools. Big Data Analytics may assist you in making better and faster decisions, forecasting future occurrences, and improving your Business Intelligence.

Big Data Analytics Tools

Let’s explore top 10 tools in detail:

  1. Xplenty

Xplenty is a cloud-based platform for data integration, processing, and preparation. All of your data sources will be brought together. Its user-friendly graphic interface will guide you through the process of implementing ETL, ELT, or replication.

Xplenty is a low-code and no-code toolkit for creating data pipelines. Marketing, sales, support, and developer solutions are all available.

Xplenty will assist you in extracting the maximum value from your data without the need to invest in hardware, software, or staff. Email, chats, phone calls, and an online meeting are all ways that Xplenty can help.


  • You’ll get quick connectivity to a range of data repositories and a full collection of out-of-the-box data transformation components with Xplenty, an elastic and scalable cloud platform.
  • You may use Xplenty’s extensive expression language to construct complicated data preparation functions, and it has an API component for advanced customization and flexibility.


  • The annual billing option is the only one offered. It does not allow you to subscribe on a monthly basis.

Pricing: For pricing information, you can request a quote. Its pricing is based on a subscription approach. You can try the platform for seven days for free.

  1. Adverity

Adverity is a configurable end-to-end marketing analytics platform that allows marketers to track marketing success in one place and discover new insights in real-time.

Adverity empowers marketers to track marketing performance in a single perspective and effortlessly uncover new insights in real-time thanks to automatic data integration from over 600 sources, rich data visualisations, and AI-powered predictive analytics.

This leads to data-driven business decisions, increased growth, and a verifiable return on investment.


  • Data integration by over 600 data sources is fully automated.
  • Concurrent data processing and transformations.
  • Reporting that is both personalised and unique.
  • A customer-centric attitude.
  • Excellent scalability and adaptability.
  • Outstanding customer service.
  • High levels of security and oversight.
  • Predictive analytics are built-in.
  • With ROI Advisor, you can easily analyse cross-channel performance.

Pricing: The subscription-based pricing model is obtainable upon appeal or request.

  1. Dataddo

Dataddo is a no-code, cloud-based ETL platform that prioritizes flexibility. With a diverse set of connections and the option to customize metrics and properties, Dataddo makes building reliable information pipelines simple and quick.

Dataddo integrates smoothly with your available data stack, so you won’t have to add any new components to your architecture or change your core procedures. Instead of wasting time learning how to utilize yet another platform, Dataddo’s straightforward UI and quick setup allow you to focus on integrating your data.


  • Has a simple user interface that is friendly to non-technical users, and can deploy data pipelines within minutes of account creation.
  • Plugs into users’ existing data stacks with ease.
  • Low-maintenance: The Dataddo team manages API modifications.
  • Within ten days after receiving a request, new connectors can be added.
  • Compliance with GDPR, SOC2, and ISO 27001 security standards.
  • When creating sources, you can choose from a variety of properties and metrics.
  • A centralized management system that monitors the status of all data pipelines at the same time.
  1. Apache Hadoop

Apache Hadoop is a software framework for handling massive data and clustered file systems. The MapReduce programming model is used to process massive data datasets.

Hadoop is a Java-based open-source framework that supports multiple operating systems.

Without a doubt, this is the best big data tool available. Hadoop is used by more than half of the Fortune 50 enterprises. Amazon Web Services, Hortonworks, IBM, Intel, Microsoft, Facebook, and others are among the big names.


  • Hadoop’s primary strength is its HDFS (Hadoop Distributed File System), which can store all types of data on the same file system, including video, pictures, JSON, XML, and plain text.
  • Excellent for research and development.
  • Allows for quick data access.
  • Highly scalable
  • Highly accessible service resting on a cluster of computers


  • Sometimes disk space matters can be confronted due to its 3x data redundancy.
  • I/O operations could have been augmented for better performance.

Pricing: This software is free to use underneath the Apache License.

  1. CDH (Cloudera Distribution for Hadoop)

CDH seeks to bring such technology to enterprise-level installations. It is completely free and open-source, with a platform distribution that includes Apache Hadoop, Apache Spark, Apache Impala, and many other tools.

You can collect, process, administer, manage, discover, model, and share a limitless amount of data using it.


  • Widespread distribution
  • Cloudera Manager does an excellent job of managing the Hadoop cluster.
  • Simple to implement.
  • Administration is simpler.
  • High levels of security and oversight.


  • There are a few strange UI aspects on the CM service, such as charts, and multiple recommended installation ways sound confusing.

On a per-node basis, however, the licensing price is somewhat high.

Pricing: Cloudera’s CDH is a free software version. However, if you’re curious about the cost of a Hadoop cluster, per-node costs range from $1000 to $2000 per terabyte.

  1. Cassandra

Apache Cassandra is a free and open-source distributed NoSQL database management system (DBMS) designed to manage large amounts of data distributed across multiple commodity servers while maintaining high availability. It communicates with the database using CQL (Cassandra Structure Language).

Accenture, American Express, Facebook, General Electric, Honeywell, Yahoo, and others are among the high-profile companies that use Cassandra.


  • There isn’t a sole point of disaster.
  • Handles large amounts of data quickly.
  • log-structured storage is a good idea.
  • Replication that is automated.
  • Scalability is linear.
  • A simple ring structure.


  • Requires extra troubleshooting and maintenance effort.
  • The clustering could’ve been better.
  • Row-level locking isn’t available.

Pricing: This tool is absolutely free.

  1. Knime

The open-source tool KNIME stands for Konstanz Information Miner which is used for Enterprise reporting, integration, research, CRM, data mining, data analytics, text mining, and business intelligence. It is compatible with Linux, OS X, and Windows.

It can be thought of as a viable alternative to SAS. Knime is used by a number of well-known companies, including Comcast, Johnson & Johnson, Canadian Tire, and others.


  • ETL processes that are simple
  • Works nicely with a variety of other technologies and languages.
  • A large number of algorithms.
  • Workflows that are both easy to use and well-organized.
  • It eliminates a lot of manual labour.
  • There are no difficulties with stability.
  • It’s simple to set up.


  • There is room for improvement in terms of data handling capacity.
  • It Takes up virtually all of the RAM.
  • Integration with graph databases could have been possible.

Pricing: Knime platform is freely available. However, they offer other commercial products which encompass the competencies of the Knime analytics platform.

  1. Datawrapper

Datawrapper is an open-source data visualization platform that allows users to quickly create simple, precise, and embeddable charts.

Its most vital consumers are news studios all across the world. The New York Times, Fortune, Mother Jones, Bloomberg, Twitter, and others are amongst the names cited.


  • Compatibility with various devices. It works great on any device, whether it’s a phone, a tablet, or a computer.
  • Completely adaptable.
  • Fast.
  • Interactive.
  • Organizes all of the charts into one convenient location.
  • Extensive customization and export capabilities.
  • Zero coding is required.

Cons: Color palettes are limited.

Pricing: It provides both free and paid services that can be customized.

  1. MongoDB

MongoDB is a document-oriented database built in C, C++, and JavaScript that uses NoSQL technology. It is an open-source utility that is free to use and supports a variety of operating systems, including Windows Vista, OS X (10.7 and later versions), Linux, Solaris, and FreeBSD.

Aggregation, Adhoc-queries, Uses BSON format, Sharding, Indexing, Replication, Server-side javascript execution, Schemaless, Capped collection, MongoDB management service (MMS), load balancing, and file storage are some of its primary characteristics.

Facebook, eBay, MetLife, Google, and further well-known companies make use of MongoDB.


  • It is simple to learn.
  • Supports a vast and diverse of technologies and platforms.
  • Installation and maintenance went through without a hitch.
  • Reliable and inexpensive.


  • Analytical capabilities are limited.
  • For some use scenarios, it’s a little slow.

Pricing: SMB and enterprise versions of MongoDB are paid, and pricing is available upon request.

  1. Lumify

Lumify is a platform for large data fusion/integration, analytics, and visualization that is free and open source.

Full-text search, 2D and 3D graph visualzations, automatic layouts, link analysis between graph elements, interaction with mapping systems, geospatial analysis, multimedia analysis, and real-time collaboration through a set of projects or workspaces are some of its most important features.


  • Scalable
  • Secure
  • A full-time development team is on hand to help.
  • The cloud-based environment is supported. It’s compatible with Amazon’s AWS.

Pricing: This tool is absolutely free.


Big data, also known as big data analytics, is one of the most commonly used terms in technology today. As you may be aware, a large volume of data is created every minute by customers and businesses all over the world, therefore big data analytics holds a lot of promise. Big data is studied by taking structured, semi-structured, and unstructured data from your data lakes and extracting what’s most relevant to your present informational demand, most likely with the use of data quality automation. Because traditional data tools were not designed to handle this degree of complexity and volume, a slew of specialized big data software tools and architectural solutions arose to meet the need. Custom-built big data analytics tools may help businesses put their data to work, discover new opportunities, and create new business models. In this post we discussed the top ten big data analytics tools utilized by big tech organizations in 2022; therefore, read the article thoroughly to gain a better consideration of market demands.