The 16 Best Data Analytics Tools for Data Analysts in 2024

01. Apache Spark

A quick overview of Apache Spark:

• Tool Type: Framework for processing data
• Accessible to everyone: Open-source
• Commonly utilized for: Processing large amounts of data; implementing machine learning algorithms
• Advantages: Rapid, flexible, and user-friendly
• Drawbacks: Absence of a file management system and a rigid user interface

Apache Spark is a powerful software framework that enables professionals in the field of data analysis to efficiently handle large volumes of data. Developed in 2012, this technology is specifically designed to analyze unstructured big data. Spark efficiently distributes computationally heavy analytics tasks across multiple computers.

Spark stands out for its exceptional speed, even when compared to other similar frameworks like Apache Hadoop. By utilizing RAM instead of local memory, the speed is approximately 100 times faster than Hadoop. That’s why it is commonly utilized in the creation of machine learning models that rely heavily on data.

It also offers a library of machine learning algorithms called MLlib, which includes various algorithms such as classification, regression, and clustering. Unfortunately, the high memory consumption of Spark results in significant computational costs. In addition, there is no file management system included, which often requires integration with other software such as Hadoop.

02. Datawrapper

A quick overview of Datawrapper:

• Tool Type: Tool for visualizing data
• Accessible to everyone: Open-source
• Commonly utilized for: Data visualization tool for everyone
• Advantages: Rapid, flexible, and user-friendly
• Drawbacks: Restrictions on customizing colors, Map creation can be quite challenging to grasp initially.

Datawrapper is a fantastic tool that specializes in creating stunning online visuals, including charts and maps. Originally designed for journalists covering news stories, its adaptability makes it appropriate for any professional responsible for website management. The tool offers a diverse selection of outputs, such as scatterplots, line charts, stacked bar charts, pie charts, range plots, and different types of maps and tables. Depending on your specific requirements, Datawrapper provides a range of options to suit your needs and budget, including both free and paid plans.

Although Datawrapper is user-friendly, it does have certain limitations that become apparent when compared to other tools in its category. One limitation is that it cannot directly integrate with data sources. This implies that users have to manually enter data into the tool, a task that can be monotonous and prone to errors if not executed with accuracy.

If you have a passion for creating web graphics and are open to the process of manually inputting data, exploring the use of Datawrapper could greatly enhance your skill set.

03. Google Analytics

A quick overview of Google Analytics:

• Tool Type: Data visualization, monitoring, reporting, predictive analysis, etc.
• Accessible to everyone: Free
• Commonly utilized for: Web Analytics Service
• Advantages: By analyzing various metrics such as website traffic, user behavior, conversion rates, and more, businesses gain valuable insights to enhance their marketing strategies and boost overall performance.
• Drawbacks: Data sampling, Data collection limits, Limited historical data, No customer support, Confusing UI, GDPR compliance, etc.

Google Analytics is a powerful tool that provides businesses with valuable insights into user behavior on their websites and apps. To utilize it, you incorporate a unique JavaScript code into your web pages. This code gathers data about visitors to your website, including the pages they view, the device they are using, and how they discovered your site. It then transmits this information to Google Analytics, where it is structured into reports. These reports provide valuable insights into patterns, such as identifying the most popular products or determining the effectiveness of different ads in driving traffic to your site.

04. Google Charts

A quick overview of Google Charts:

• Tool Type: Data visualization, monitoring, reporting, predictive analysis, etc.
• Accessible to everyone: Free
• Commonly utilized for: Data visualization
• Advantages: BI Standard Reporting, Ad-hoc Reporting, Report Output and Scheduling, Data Discovery and Visualization, Access Control and Security, Mobile Capabilities, Application Program Interfaces (APIs) / Embedding.
• Drawbacks: No Free Trial, Premium Consulting/Integration Services.

Google Charts is an excellent online tool that can create a variety of interactive and captivating data visualizations. The design of this tool focuses on being user-friendly, providing a wide range of pre-set chart types that can easily be embedded into web pages or applications. The flexible capabilities of Google Charts enable seamless integration with various web platforms, such as iPhone, iPad, and Android, expanding its reach to a wider audience.

This tool is perfect for those who want to create visually appealing data visuals for web and mobile platforms. It offers a high level of customization and is easy to use. It’s also a fantastic choice if you want to showcase your charts. The seamless integration allows you to easily publish them on various web platforms by sharing a link or embedding it into a website’s HTML code.

05. Google Cloud AutoML

A quick overview of Google Cloud AutoML:

• Tool Type: Machine learning platform
• Accessible to everyone: Cloud-based, Commercial
• Commonly utilized for: Automating machine learning tasks
• Advantages: Enables analysts with minimal coding knowledge to create and implement machine learning models, bypassing numerous steps.
• Drawbacks: The cost may be high for extensive projects and there may be limitations in terms of flexibility.

Google Cloud’s AutoML tool is a significant proposition for data analysts and scientists in 2024. In 2023, the excitement surrounding generative AI is expected to continue into the next year. Tools such as AutoML empower individuals to take control of creating their own machine learning models.

Google Cloud AutoML offers a range of tools that cover various categories such as structured data, language translation, and image and video classification. With the increasing adoption of machine learning in organizations, there will be a rising need for professionals who can effectively utilize AutoML tools to streamline their tasks.

06. Jupyter Notebook

A quick overview of Jupyter Notebook:

• Tool Type: Interactive authoring software.
• Accessible to everyone: Open-source.
• Commonly utilized for: Collaborating on code, generating instructional content, and showcasing projects.
• Advantages: Excellent for demonstrating abilities, not limited by programming language.
• Drawbacks: Lacks self-containment and isn’t ideal for collaboration.

Jupyter Notebook is a fantastic open-source web application that enables you to create interactive documents effortlessly. This combination incorporates live code, equations, visualizations, and narrative text.

Imagine a software application that resembles a Microsoft Word document but with enhanced interactivity and a specialized focus on data analytics! It excels at presenting work, just like a computer systems analyst. Jupyter Notebook operates within a web browser and supports over 40 programming languages, like Python and R. In addition, it seamlessly integrates with popular big data analysis tools such as Apache Spark (refer to the section below). It provides various output options, including HTML, images, videos, and more.

However, like any tool, it does have its limitations. Version control in Jupyter Notebook documents is lacking, and keeping track of changes can be challenging. This indicates that it may not be the ideal environment for development and analytics tasks (it is recommended to utilize a specialized IDE for these purposes) and may not be well suited for collaborative work.

As it is not self-contained, you must provide additional assets (such as libraries or runtime systems) to anyone you share the document with. However, for presentation and tutorial purposes, it continues to be an indispensable tool for data science and data analytics.

07. KNIME

A quick overview of KNIME:

• Tool Type: Interactive authoring software.
• Accessible to everyone: Open-source.
• Commonly utilized for: Data mining and machine learning.
• Advantages: The platform is open-source and is particularly well-suited for visually-driven programming.
• Drawbacks: Limited ability to handle larger workloads, and certain tasks require specialized technical knowledge.

KNIME (Konstanz Information Miner) is an open-source, cloud-based data integration platform created in 2004 by software engineers at Konstanz University in Germany. Originally developed for the pharmaceutical industry, KNIME’s ability to gather data from multiple sources and consolidate it into a unified system has led to its adoption in various other fields. These involve analyzing customer data, utilizing business intelligence tools, and applying machine learning techniques.

Aside from being free, one of its main attractions is its user-friendly interface. Its drag-and-drop graphical user interface (GUI) is perfect for visual programming. Users can easily create data workflows without requiring extensive technical expertise. Although it purports to be capable of handling various data analytics tasks, its true expertise lies in data mining. Having a basic understanding of Python and R will be helpful for users, as they offer in-depth statistical analysis as well. KNIME is highly adaptable and can be tailored to meet the specific requirements of any organization, all while remaining cost-effective. Small businesses find it appealing due to its affordability.

08. Microsoft Excel

A quick overview of Microsoft Excel:

• Tool Type: Spreadsheet software.
• Accessible to everyone: Commercial.
• Commonly utilized for: Data manipulation and generating reports.
• Advantages: This software is widely utilized and offers a wide range of useful features and add-ons.
• Drawbacks: Expenses, inaccuracies in calculations, limitations with large datasets.

Excel: a widely recognized and popular spreadsheet software. In addition, it offers calculations and graphing functions that are perfect for data analysis.

Excel is an essential tool in the industry regardless of your area of expertise and the additional software you may require. Its impressive built-in features allow you to easily sort and total data using pivot tables and create forms effortlessly.

In addition, it offers a range of other features that enhance data manipulation efficiency. For example, the CONCATENATE function enables you to merge text, numbers, and dates into a single cell. With the help of SUMIF, you can effortlessly generate value totals by applying different criteria. Additionally, Excel’s search function simplifies the process of isolating specific data.

However, there are certain limitations to consider. For example, it operates sluggishly when handling large datasets and tends to make approximations with big numbers, resulting in inaccuracies. However, this tool is crucial and has significant capabilities for data analysis. Additionally, numerous plug-ins can help overcome any limitations of Excel. Begin your journey with these ten essential Excel formulas that every data analyst should be familiar with.

09. Microsoft Power BI

A quick overview of Microsoft Power BI:

• Tool Type: Business analytics suite.
• Accessible to everyone: Commercial software (with a free version available).
• Commonly utilized for: Data visualization and predictive analytics.
• Advantages: Excellent data connectivity, frequent updates, and impressive visualizations.
• Drawbacks: The user interface could be more user-friendly, the formulas are inflexible, and the free version has data limitations.

Power BI, being a relatively new addition to the data analytics tools market, has been around for less than a decade. It originally started as an Excel plug-in, but it was later transformed into a comprehensive set of independent business data analysis tools in the early 2010s. With Power BI, users can effortlessly create dynamic visual reports and dashboards, making data analysis a breeze. One of its standout features is its excellent data connectivity. It seamlessly integrates with various platforms such as Excel, text files, SQL servers, and popular cloud sources like Google and Facebook analytics.

It also provides robust data visualization capabilities, although other aspects have the potential for enhancement. For instance, the user interface is quite bulky, the formulas are rigid, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not user-friendly. Multiple subscription options are available, including a complimentary one. This is excellent for those who want to familiarize themselves with the tool, although the free version does have its drawbacks. The primary limitation is the low data limit, which is approximately 2GB.

10. Python

A quick overview of Python:

• Tool Type: Programming language.
• Accessible to everyone: Open-source, with a wide range of free libraries.
• Commonly utilized for: a wide range of tasks, including data scraping, analysis, and reporting.
• Advantages: Easy to learn, extremely adaptable, extensively utilized.
• Drawbacks: Requires more memory and may not execute as quickly as specific other languages.

Python is incredibly versatile, with extensive resource libraries catering to various data analytics tasks. For instance, the NumPy and Pandas libraries optimize complex calculations and facilitate data manipulation.

Tools such as Beautiful Soup and Scrapy are commonly employed for web data scraping, while Matplotlib is highly regarded for its data visualization and reporting capabilities. Python’s speed is a limitation, as it requires more memory and is slower than other languages. Generally speaking, Python’s advantages outweigh any disadvantages when developing software from the ground up. Our comprehensive guide provides extensive information on Python.

11. QlikView

A quick overview of QlikView:

• Tool Type: Programming language.
• Accessible to everyone: Open-source, with a wide range of free libraries.
• Commonly utilized for: Data Integration, Data Analytics, and Developer platforms.
• Advantages: Quick Data Visualization, Drag-and-Drop Functionality, Robust Analytics.
• Drawbacks: Steep Learning Curve, Limited Customization, Higher Cost.

QlikView is a powerful tool for self-service business intelligence, data visualization, and data analytics. Recognized as a top performer in the Gartner Magic Quadrant 2024 for Analytics and BI platforms, this solution is designed to enhance business outcomes by leveraging data. It offers a range of capabilities, including Data Integration, Data Literacy, and Data Analytics.

QlikView has introduced a new platform called Qlik Alerting for Qlik Sense®. This platform assists organizations in managing exceptions, notifying users of potential issues, conducting further analysis, and taking actions based on insights.

12. R

A quick overview of R:

• Tool Type: Programming language.
• Accessible to everyone: Open-source, with a wide range of free libraries.
• Commonly utilized for: Statistical analysis and data mining.
• Advantages: It works on any operating system, is very compatible, and offers a wide range of packages.
• Drawbacks: It is not as fast, lacks the same level of security, and has a steeper learning curve compared to Python.

R, similar to Python, is a widely used open-source programming language. It is often used to develop software for statistical and data analysis purposes.

R’s syntax is more intricate compared to Python, and it requires a bit more effort to grasp. However, it was designed with a focus on handling complex statistical computations and is widely used for visualizing data. Similar to Python, R also has a vast collection of freely available code known as CRAN (the Comprehensive R Archive Network), providing access to over 10,000 packages.

It seamlessly integrates with various languages and systems, including big data software, allowing it to utilize C, C++, and FORTRAN code. Unfortunately, this system’s memory management is lacking, and although there is a helpful community of users available for assistance, there is no dedicated support team for R. However, a fantastic R-specific integrated development environment (IDE) called RStudio is definitely a great advantage!

13. RapidMiner

A quick overview of RapidMiner:

• Tool Type: Data mining.
• Accessible to everyone: Open-source, with a wide range of free libraries.
• Commonly utilized for: Statistical analysis and data mining.
• Advantages: Intuitive User Interface, Versatile Operators, Extensive Support System.
• Drawbacks: Outdated User Interface, Difficulties in Finding Key Operators, Lack of Documentation for Operators.

RapidMiner is a powerful tool for data mining and model development. This platform enables professionals to efficiently handle data at various stages, such as preparation, visualization, and review. This can be advantageous for professionals who work with non-raw data or have previously extracted information.

RapidMiner also provides a wide range of algorithms for classification, regression, clustering, and association rule mining. Although it has certain limitations in feature engineering and selection, it makes up for them with a robust graphical programming language.

This software is versatile and can be used by individuals from diverse backgrounds. It can be applied in a wide range of industries, including manufacturing, life sciences, energy, and health care. If you have experience working with previously mined data, this software can be a valuable tool for researchers or data scientists who analyze historical data.

14. SAS

A quick overview of SAS:

• Tool Type: Statistical software suite.
• Accessible to everyone: Commercial.
• Commonly utilized for: Business intelligence, multivariate, and predictive analysis.
• Advantages: Conveniently available, geared towards business needs, excellent user assistance.
• Drawbacks: Expensive and lacking in visual presentation.

SAS is a widely used commercial suite of business intelligence and data analysis tools. The SAS Institute created it in the 1960s and has continued to evolve. Its primary application in modern times involves customer profiling, reporting, data mining, and predictive modeling. Designed with the needs of large organizations in mind, the software is known for its robustness, versatility, and user-friendly interface. This is because organizations often have different levels of programming expertise in-house.

However, as a commercial product, SAS does come with a substantial price tag. However, there are advantages to consider as well. The system is constantly updated with new modules to meet customer needs. While the number of these may be fewer compared to Python libraries, they are highly specialized. For example, it provides modules for specific purposes, such as detecting money laundering and analyzing data for the Internet of Things.

15. Splunk

A quick overview of Splunk:

• Tool Type: Search, Analyze, and Visualize the data.
• Accessible to everyone: Free, Enterprise, Cloud.
• Commonly utilized for: Operations monitoring, Application performance monitoring, Business analytics, and Log management.
• Advantages: Efficient, live monitoring with a diverse range of compatible add-ons.
• Drawbacks: Expensive, Challenging to Learn, Requires a Lot of Resources.

Splunk is a powerful platform that efficiently searches, analyzes, and visualizes machine-generated data collected from various sources, such as applications and websites. Gartner recognized Splunk as a Visionary in the 2024 Magic Quadrant for APM. Over the years, Splunk has developed a range of products in areas including IT, Security, DevOps, and Analytics.

With a strong track record of serving top-tier companies, Splunk is the go-to solution for industry leaders like Dominos, Otto Group, Intel, and Lenovo. These companies rely on Splunk to uncover valuable insights, analyze data in real time, and streamline their operations.

Given the widespread need for data management in organizations, Splunk strives to make data accessible throughout your entire organization. It accomplishes this by enabling teams to utilize Splunk for proactive problem prevention and prediction, effective issue detection and diagnosis, comprehensive exploration and visualization of business processes, and seamless optimization of security measures.

16. Tableau

A quick overview of Tableau:

• Tool Type: Data visualization tool.
• Accessible to everyone: Commercial.
• Commonly utilized for: Generating data dashboards and worksheets.
• Advantages: Impressive visual representations, fast performance, interactive features, and compatibility with mobile devices.
• Drawbacks: Limited version control capabilities and lack of data pre-processing functionality.

Tableau is a top-notch commercial data analysis tool if you want to create interactive visualizations and dashboards without needing advanced coding skills. The suite excels at handling large amounts of data, surpassing many other BI tools in this regard. Additionally, it boasts a user-friendly interface that is easy to navigate. It offers a user-friendly drag-and-drop interface, which sets it apart from other data analysis tools. Nevertheless, due to the absence of a scripting layer, Tableau’s capabilities are somewhat limited. For example, it is not ideal for data pre-processing or developing intricate calculations.

Although it does have functions for manipulating data, they are not very impressive. Typically, scripting functions using Python or R are performed prior to importing data into Tableau. However, its visualization is exceptionally impressive, which has contributed to its widespread popularity despite its limitations. In addition, it is optimized for mobile devices. If you’re a data analyst, mobility may not be your top concern, but it can be convenient if you want to work on the go! Discover additional information about Tableau in this post.