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Best Data Analytics Tools in 2024 and Beyond

Data Analytics Tools

Today, data analytics is a gigantic market and growing. One of the factors driving this growth is the rising number of data analytics tools and software. Several of these tools feature prominently in the daily schedule of a data analyst. If you’re aspiring to become one, this guide will familiarize you with the critical tools of the trade.

Read on as we explore some of the most popular data analytic tools, what they do, their pros and cons, and pricing. For a deeper dive into the subject, you sign up for a data analytics course and continue your learning.


Tableau, a popular BI tool built by Salesforce, lets you create super-interactive dashboards and visualization without having hardcore coding expertise or technical know-how. It is known for its speed, ease of use, interactivity, and mobile compatibility. However, it lacks pre-processing capabilities for data and may have challenges with version control. Despite its drawbacks, Tableau continues to be a well-loved and widely-used tool.


Some of the most prized features of Tableau include:

  • Fast analytics
  • Interaactive dashboards
  • Automatic updates
  • User-friendly interface
  • Data exploration
  • Real-time sharing on the web and mobile devices

Tableau also offers many products based on the task you want to perform. These include:

  • Tableau Desktop – It’s the core product for creating interactive visualizations.
  • Tableau Server – This web product facilitates collaboration and sharing within an organization.
  • Tableau Online – This is Tableau’s cloud-based platform that performs similarly to the server version.
  • Tableau Reader – This product allows users to view and interact with Tableau Desktop files.
  • Tableau Public – It’s a free version for creating and sharing visualizations publicly but comes with limited functionalities.

Companies Using Tableau

Many of the world’s biggest names use Tableau for data analysis. Amazon, Skype, LinkedIn, Citibank, Deloitte, and Audi are some of them.


Tableau offers various product pricing options, with Tableau Public being free. The paid version of this tool starts from 15-70$ per month.

Also Read: 28 Data Analyst Interview Questions to Help You Prepare


Python is one of the fastest programming languages globally, with applications in Software Development, Data Science, Machine Learning, and more. For data analysts, Python’s merit lies in its versatility and readability. It offers many libraries for data analytics tasks, including computational tasks, web scraping, and data visualization. Despite being slower and memory-intensive compared to some languages, Python’s widespread popularity in the tech industry makes it a familiar choice for many programmers.


  • Python is an Object-Oriented Programming language known for its ease of learning.
  • It boasts a rich set of libraries, making it a popular choice for Data Analytics.
  • Prominent libraries like Pandas and NumPy are extensively used for data manipulation, visualization, numerical analysis, and merging.
  • Python’s usage extends across a wide range of industries due to its versatility.

Companies Using Python

Google, Netflix, Reddit, and Spotify are among many companies that use Python.


Python is an open-source programming language, so it is free to use.


R is a popular open-source programming language, similar to Python, widely recognized for its role in creating statistical and data analysis software. While its syntax is more complex and has a steeper learning curve than Python, R excels in heavy statistical computing tasks and data visualization.

R also boasts a vast repository of freely available code known as CRAN (the Comprehensive R Archive Network), housing over 10,000 packages for various purposes. R integrates seamlessly with other languages and systems, including big data software, and can leverage code from languages such as C and C++. However, it faces challenges in memory management. It lacks a dedicated support team, though it benefits from an excellent R-specific integrated development environment (IDE) known as RStudio, which can be a significant advantage for users.


  • R excels in Exploratory Data Analysis (EDA), which involves summarizing data characteristics, often using visual methods.
  • Packages like plyr, dplyr, and tidy simplify data manipulation in R.
  • It offers data analysis and visualization capabilities with packages like ggplot, lattice, ggvis, and more.

Companies Using R

Businesses across industries use R, including some of the biggest enterprises worldwide, namely Google, Facebook, Bing, Microsoft, Barclays, Accenture, IBM and American Express


R is an open-source programming language, and it is freely available.

Power BI

Although relatively new, Power BI has quickly established itself as a data analytics tool focusing on user-friendly features. Originally conceived as an Excel plug-in, it underwent redevelopment in the early 2010s to become an independent suite of business data analysis tools.

Power BI stands out for its ability to empower users to create interactive visual reports and dashboards with a shallow learning curve. Its primary strength lies in seamless data connectivity, encompassing Excel and various data sources like text files, SQL servers, and cloud platforms like Google and Facebook analytics.

While Power BI excels in data visualization, some areas require improvement. It is often criticized for its somewhat cumbersome user interface, rigid formula implementation, and the less user-friendly proprietary language, Data Analytics Expressions (DAX).


  • Power BI allows users to create interactive and visually appealing reports and dashboards.
  • Users get a wide range of visualization options to represent data effectively.
  • It integrates seamlessly with other Microsoft products and services, enhancing its compatibility with Microsoft-centric environments.
  • It connects to various data sources, including Excel, text files, SQL servers, and cloud platforms like Google and Facebook Analytics.

Companies Using Power BI

  • Adobe
  • Heathrow
  • Worldsmart
  • GE Healthcare


Power BI offers three subscription options: Desktop, Pro, and Premium. The Desktop version is free and provides an excellent starting point for users. However, it has some restrictions, notably a data limit of around 2GB. Power BI Pro prices for Premium SKUs range from $10 to $5,000 monthly.

Also Read: How Much is the Typical Data Analytics Salary? A Complete Guide


QlikView is a leading player in business analytics, offering a Software as a Service (SaaS) solution known as QlikView Software. This platform facilitates the swift creation and deployment of interactive guided analytics applications and dashboards.

QlikView offers quick data visualization, drag-and-drop functionality for ease of use, and robust analytics capabilities. Users may encounter a steep learning curve, limited customization options, and potentially higher costs.


  • QlikView’s product lineup includes Qlik Sense and Qlik Replicate, both cloud-based software solutions for business intelligence and data integration.
  • It empowers users to accelerate business value through data analysis, offering features like data integration, literacy, and analytics.
  • A significant feature introduced by QlikView is Qlik Alerting for Qlik Sense, an intelligent alerting platform. This platform helps organizations manage exceptions, notify users about potential issues, and prompt actions based on derived insights.
  • QlikView emphasizes data integration and literacy as key components, enabling users to seamlessly work with data and gain a deeper understanding.

Companies Using QlikView

Cisco, KitchenAid, Samsung, and Mercedes-Benz are among the most prominent users of this tool.


QlikView offers a variety of products and services with pricing options depending on specific functionalities and usage. It starts from $20 per user to $2700 monthly for organizations.

Apache Spark

Apache Spark is a robust software framework designed to handle vast datasets swiftly, making it a preferred choice for data analysts and scientists. Introduced in 2012, it was later contributed to the Apache Software Foundation as an open-source project. Spark’s distinguishing feature lies in its exceptional speed, leveraging RAM for operations, which makes it approximately 100 times faster than alternatives like Apache Hadoop. This speed is particularly advantageous in data-heavy machine learning model development.

Spark further boasts a library of machine learning algorithms, known as MLlib, encompassing various algorithms for classification, regression, clustering, and more. However, its high memory consumption renders it computationally expensive, and it lacks an integrated file management system, often necessitating integration with other software, such as Hadoop, to fulfill comprehensive data processing needs.


Apache Spark functions as a cluster computing framework designed for real-time data processing. Spark boasts a vibrant open-source community and offers programming interfaces ensuring fault tolerance and implicit data parallelism.

Companies Using Apache Spark

Oracle, Verizon, Visa, Shopify, Uber, Slack, and many other companies use Spark in their data-related operations.


  • Spark adapts to contemporary computing environments
  • It runs seamlessly on Kubernetes, Apache Mesos, standalone setups, Hadoop clusters, or in the cloud.
  • It provides high-level APIs for Java, Scala, Python, and R,
  • It allows flexibility in code development.
  • Spark’s MLlib is especially valuable for Big Data processing.


Apache Spark is an open-source project and is free to use.


RapidMiner is a versatile data science software platform that provides an integrated environment for data preparation, machine learning, deep learning, text mining, predictive analytics, and more. Its adaptability extends to various applications, encompassing business and commercial development, research, education, training, rapid prototyping, and general application creation.

Notably, RapidMiner supports all facets of the machine learning process, from data preparation and visualization to model validation and optimization. Operating on an open-core model, it enables the monetization of commercially produced open-source software. With its integrated environment, it serves as a valuable tool for diverse data-related tasks and industries.


  • RapidMiner operates using a client and server model, with the server deployable on-premises or in public or private cloud infrastructures
  • RapidMiner 9.6, the latest version, extends the platform’s capabilities to full-time coders and business intelligence users. It offers a transparent, end-to-end Data Science platform for data preparation, Machine Learning, and model operations.
  • RapidMiner’s functionality can be expanded using additional extensions, such as the Deep Learning or Text Mining extensions, accessible through the RapidMiner Marketplace.
  • The RapidMiner Marketplace is a platform for developers to create data analysis algorithms and share them with the community. Recently, it has been extended to full-time coders and BI Users, making it a fully transparent, end-to-end Data Science platform capable of handling data preparation, Machine Learning, and model operations.

Companies Using RapidMiner

BMW, Hewlett-Packard, and Sanofi are some of the top companies that use this tool.


RapidMiner offers a range of products, each with its own features and prices starting from $7500.

Also Read: What is Data Analytics: Types, Roles, and Techniques


KNIME is an open-source, cloud-based data integration platform developed in 2004 by software engineers at Konstanz University in Germany. While initially intended for the pharmaceutical industry, its versatility in consolidating data from various sources has led to its application in customer analysis, business intelligence, and machine learning across different domains. This user-friendly tool is accessible even for users with limited technical expertise. This ease of use is a key advantage for creating data workflows.

While KNIME claims to support a wide range of data analytics tasks, it excels in data mining. Although it offers in-depth statistical analysis capabilities, users can benefit from some knowledge of Python and R for specific tasks.

Being open-source, this highly flexible and customizable tool can meet an organization’s specific needs without incurring heavy costs. This adaptability makes it particularly popular among smaller businesses operating with limited budgets.


  • The KNIME Analytics Platform is an open-source software that cleans and gathers data. It is also used to make reusable components accessible to everyone and create Data Science workflows.
  • KNIME Server is a platform enterprises can use to deploy data science workflows, team collaboration, management, and more.
  • KNIME provides a simple, easy-to-use drag-and-drop graphical user interface (GUI), making it ideal for visual programming (Visual programming is a programming language that helps humans describe processes using illustration).
  • It offers in-depth statistical analysis, and no technical expertise is required to create workflows for data analytics in KNIME.
  • KNIME allows the creation of reusable components, making it easier to streamline and reuse parts of data science workflows.

Companies Using KNIME

Top companies like Siemens, Novartis, and Deutsche Telekom use KNIME for data analytics.


KNIME is an open-source platform, and its core functionalities are free of cost. But, for enterprises, the prices start from $ 5,000 annually for five licenses.

Factors to Consider When Picking a Data Analysis Tool

  • Diversify your tool selection: If you want your hunt for a data analytics tool to succeed, you must first recognize the need for multiple data analysis tools. No tool can address your organization’s various data analysis requirements. Therefore, a business must be prepared to invest in at least two tools—a primary tool for most tasks and a secondary one for smaller processes.
  • Evaluate user needs: Who will be using the tool? Assess your organization’s business needs and identify the intended user base for data analysis tools, including data analysts, scientists, non-technical users, or a combination.
  • Data modeling capabilities: How good are your chosen tool’s data modeling capabilities? Does it support data modeling, or will you need supplementary tools like SQL for this purpose? These answers will help you make a better selection.
  • Scalability and performance: Ensure that the chosen data analytics tool is scalable and capable of handling growing data volumes while maintaining efficient performance to adapt to evolving data requirements.
  • Integration and compatibility: It’s important to assess how well the selected data analysis tool integrates with existing systems and its compatibility with intended data sources, as integration challenges can significantly affect its effectiveness.
  • Price and licensing: Draw a list of pricing and licensing models of your chosen tools and see which ones match your budget and business requirements. Some tools offer free features with paid options, while others require subscriptions or licenses—factor in the number of users and subscription durations for project-specific needs.

Take the Next Step

Now that you’re familiar with the best data analytics tools in the current market, it’s clear that no one tool can perform every task. Therefore, a deep understanding of a broad range of tools is crucial to navigate the vast world of data.

But here’s the exciting part – the journey doesn’t end here. If you’re an aspiring data analyst or a professional looking to bolster your skills, you’ve only scratched the surface. With a Data Analytics Bootcamp, you can unlock the full potential of these tools. Such courses teach you the ins and outs of data analysis and provide you with hands-on experience to master these powerful tools.


1. What are the most widely used data analytics tools?

R, Python, Microsoft Excel, Power BI, and Tableau are some of the most used tools.

2. What tools are essential for data analysis?

Essential tools for data analytics include Microsoft Excel, R, Python, Tableau, Power BI, Apache Spark, and more.

3. Is Tableau a data analytics tool?

Tableau is a data analytics tool widely used for data visualization and analysis.

4. How do I choose a data analytics tool?

When choosing a data analytics tool, consider your specific needs. This includes the type of data you work with, your budget, and your team’s expertise level.

You might also like to read:

Data Scientist vs. Data Analyst – The Differences Explained

Data Scientist Salaries: Here’s What You Need to Know

A Data Scientist Job Description: The Roles and Responsibilities

How to Become a Data Scientist?

Top Data Scientist Skills You Must Have

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