Gaining an advantage in business has always required making well-informed decisions and taking advantage of gaps in the market and openings. That refers to data analytics technologies in the era of information. Data analysis has grown commonplace, especially in the context of business intelligence. Community-driven solutions are becoming a genuine alternative to proprietary ones in the market, with thousands of users and contributors supporting their infrastructure. Read more to know about 10 best open source data analytics tools that will help you in analyzing data: 

Open-Source Data Analytics tools: What Is It?

Open-source data analytics tools is a software with a source code that anyone can modify, inspect or enhance. The open-source data analytics tools given below provide a wide range of functionalities for various users. It’s crucial to keep in mind that some of the open-source options on this list require some developmental skills.

1.Zoho Analytics

Users may generate data visualizations and dashboards using Zoho Analytics, formerly known as Zoho Reports. It is a self-service BI and data analytics tool. Zoho’s Personal free on-prem edition allows for an unlimited number of reports and dashboards, as well as customizable dashboard features and API support. However, it only supports one user. Additionally, when you register for the free tool, you get to try Professional Edition for one month. Linux and Windows both support Zoho Analytics.

Key Features

  • A large selection of visualization choices, including charts, pivot tables, summary views, KPI widgets, and dashboards with custom themes.
  • Enhanced analytics employing an intelligent assistant driven by AI and ML that can comprehend questions posed in natural language.
  • Sleek mobile applications for Android and iOS that allow for the interactive analysis of dynamic data.
  • Cooperative analytics with granular access management.
  • Intelligent modeling to link relevant data tables.
  • Data blending for cross-functional analytics from various data sources.

2.Tableau Public

Tableau public is one of the most popular and market-dominating business intelligence tools. It is used to analyze and visualize data in an incredibly user-friendly manner. You don’t need a lot of coding experience or technical understanding. It is a commercially available technology that can be used to create incredibly interactive data visualization and dashboards. 

Key Features

  • It offers automatic layouts for phones and tablets.
  • It gives you the option to alter these layouts.
  • Transparent highlighters, parameters, and filters are all programmable.
  • The dashboard zone preview is visible.
  • You can combine datasets based on a location.
  • You can connect to cloud databases.
  • Tableau Prep has features like instant results, which let you pick and change values right now. provides cloud-based data integration and ETL solution. With the adherence to compliance best practices, it offers strong on-platform transformation capabilities. It assists with cleaning, normalizing, and transforming their data. You will be able to build straightforward, visually appealing data pipelines for your data warehouse.

Key Features

  • Effective low-code data transformation solution.
  • Import information from any source that offers a RestAPI. With’s API Generator, you can make your own RestAPI if there isn’t one already available.
  • Send data to databases, on-premises data warehouses, NetSuite, and Salesforce. 
  • links to all of the main e-commerce platforms.
  • gives priority to customer assistance and feedback and has security features. Like field-level data encryption, SOC II certification, GDPR compliance, and data masking to meet all compliance needs.


KNIME is an open-source analytics platform that can be used to build data science services and applications. Users can build visual workflows using the tool’s drag-and-drop graphical interface, which is open and constantly integrates new advancements. By modeling each phase of your study and managing the flow of data, you can design your workflow using more than 2000 module nodes. Large projects can also be accessed through open-source connectors for KNIME. And community extensions are user-contributed features from programs designed for a particular industry.

Key Features

  • It offers a GUI where you can construct visual processes using the drag-and-drop feature.
  • No coding expertise is required.
  • It enables you to combine tools from several fields, such as machine learning, Apache Spark connectors, and R and Python programming.
  • Instructions for creating workflows.
  • Memory-based processing and Data visualization using sophisticated charts.
  • It enables you to modify the charts to meet your needs.


RapidMiner is a data science software platform that was created to provide an integrated environment. It is to provide data preparation, machine learning, deep learning, text mining, predictive analytics, etc. Applications for business and commerce can be created with RapidMiner. It can also be used for application development, quick prototyping, education, and research. Additionally, it covers all phases of machine learning, including data preparation, display of the outcomes, model validation, and model optimization. RapidMiner is being developed using an open core approach.

Key Features

  • Integrated security controls.
  • Radoop does away with the need for writing code.
  • Hadoop and Sparx visual workflow designers are available.
  • Radoop makes it possible to leverage huge datasets for Hadoop training.
  • Centralized control of the workflow.
  • It supports sentry/ranger, Hadoop impersonation, and Kerberos.
  • It groups the requests and makes use of Spark containers again for clever process optimization.

6.Power BI

Microsoft’s latest data analytics solution, Power BI, was introduced in 2011. Power BI, which is a component of the Microsoft Power Platform, intends to offer interactive visualizations. And business intelligence capabilities with a user interface that is straightforward enough for end users to autonomously produce their own reports and dashboards. In addition to numerous other applications, it can be used for data visualization and predictive analysis. 

Key Features

  • There are three versions of Power BI: Desktop, Pro, and Premium. The Desktop version is cost-free. However, the other two need payment.
  • It enables the import of data to shareable live dashboards and reports.
  • It can be easily integrated with cloud service.

7.Apache Spark

Apache Spark employs the data processing framework, big data processing, and machine learning frequently. It enables data analysts to quickly process very large datasets. Using Apache Spark, it is quite simple to examine massive data and carry out computationally intensive analytics on them. One of the most active Apache projects currently is Spark. It has a fantastic open source community and a programming interface that supports implicit data parallelism and fault tolerance.

Key Features

  • Running an application in a Hadoop cluster is beneficial since it can run up to 100 times quicker in memory and ten times faster on disc.
  • It is one of the open source big data analytics tools that offer built-in APIs in Java, Scala, or Python. And supports sophisticated analytics, integrates with Hadoop, and can use existing Hadoop data. 
  • It is one of the open source data analytics tools that delivers lightning-fast processing.


Talend was created on the Eclipse graphical programming environment and is one of the most potent data integration ETL technologies on the market. This tool allows you to effortlessly manage all the phases involved in the ETL process. It also seeks to offer compliant, accessible, and clean data for everyone. 

Key Features

  • Talend Open Source, Talend Pipeline Designer, Stitch Data Loader, Talend Cloud Data Integration, and Talend Data Fabric are a few of the company’s products. 
  • Talend can assist in providing us with accurate and complete data when we need it. It accomplishes this by upholding data quality, offering Big Data integration, cloud API services, etc., 
  • Additionally, it offers Data Catalog and Stitch Data Loader and prepares data. In order to uncover insight into data, Talend began to follow the lakehouse model.


QlikView is a self-service solution for business intelligence, data visualization, and data analytics tools. With capabilities like Data Integration, Data Literacy, and Data Analytics, it strives to accelerate the value that data can bring to businesses.

Key Features

  • It is a self-service business intelligence and data analytics solution that offers capabilities like data integration and data analytics to help businesses gain more value from their data faster.
  • The intelligent alerting platform Qlik Alerting for Qlik Sense is one of the most significant features introduced by QlikView. This assists businesses in handling exceptions and alerting users to potential problems. Additionally, it supports users in their analysis and suggests actions depending on the conclusions reached.


The fastest, most beautiful method for making dynamic, visually appealing data visualizations and presentations is present in Juicebox. Juicebox differs from other visualization tools with a focus on data storytelling and usability. The pricing structure is cost-effective for groups and free for individuals.

Key Features

  • A distinct data narrative strategy.
  • Simple to learn to edit
  • Simple to configure interactive data visualizations.
  • Simple styling options guarantee a polished appearance.
  • Visualizations are automatically connected for data exploration at a deeper level.
  • Establish connections to numerous data sources using database connections or data uploads.
  • Mobile-friendly responsive design.

Bottom Line

The best tools for performing data analysis are listed above. When selecting one of these tools, you have to consider many criteria, including license fees and protocol support.