10 Best Open Source Data Analytics Tools 2024

If you want to get ahead of the pack in commerce, you’ll need to make informed decisions and capitalize on opportunities and inefficiencies around your business.

The digital age has made it so much easier to do that with the introduction of data analytics tools.

Analyzing data has become the norm in 2020 and beyond, so much so that it’s diffused enough to get to free and open-source data analytics tools for any level.

However, there are so many open-source data analytics tools on the market, which means you need to choose them wisely in order to benefit from your analytics efforts.

Here’s a roundup of our top picks for the best open-source data analytics tools you can use to develop and perform analytical processes and make better, informed business decisions.

Best Open Source Data Analytics Tools

1. Grafana

Grafana is an open-source data analytics platform that allows you to monitor and observe metrics across different apps and databases. You get alerts that notify you when specific events happen along with real-time insights into external systems.

The software is commonly used by DevOps engineers to monitor their systems, run analytics, and pull up metrics that make sense of big data all with the help of customizable dashboards.

With Grafana, you can visualize your data using geomaps, heatmaps, graphs, and histograms, making it easier to understand your data. You also get to bring your data together for better context and seamlessly define alerts where it makes sense.

The software gives you options to use like Cloud or you can install it easily on any platform. Plus, you can discover hundreds of plugins and dashboards in its official library and bring your team together to share data and dashboards.

Grafana supports more than 30 other open-source and commercial sources of data so you can pull data from wherever it lives. You also get a built-in Graphite query parser that makes it easier to read and edit expressions faster than ever.

The software also integrates easily into your workflow and you can roll it into your product or service offerings.

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2. Redash

Redash is another popular open-source data analytics tool that helps organizations become more data-driven. The software provides features that help you connect to any data source, visualize and share your data, and democratize data access with your company.

You can customize and add features without worrying about lock-ins, query data sources, and enjoy powerful collaboration with your colleagues.

The tool helps you create amazing dashboards so you can easily visualize your results in cohorts, charts, pivots, tables, maps, and more. Plus, you can gather information from various sources and share your dashboards or data stories with colleagues on a URL or embed widgets wherever you need them.

Redash also lets you set up alerts and get notified of events based on your data. If you want more functionality, you can access the tool via an API.

User Management is included with SSO, access control, and other features that make for an enterprise-friendly workflow.

The tool is cost-effective and lightweight, and although it’s open-source, an affordable hosted version is available if you want to start using it ASAP.

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First released in 2006, KNIME’s Analytics platform has quickly been adopted by the open-source community, companies, and software vendors who use it to create data science. The open and intuitive software makes understanding data easy.

You can create visual workflows using the drag and drop graphical user interface, model your analytical steps while controlling data flow, and ensure your work is current.

Plus, you can blend tools using KNIME native nodes from different domains into one workflow. You can also access and retrieve data from AWS S3, Salesforce, Azure, and other sources.

When your data is ready, you can shape it by deriving statistics, aggregating, sorting, filtering, and joining data in a database, distributed big data environments, or on your local machine.

The KNIME Analytics Platform also leverages machine learning and artificial intelligence to build machine learning models for regression, classification, clustering, or dimension reduction. The tool also helps you optimize model performance, validate models, explain machine learning models, and make predictions using industry-leading PMML or validated models directly.

KNIME also lets you visualize your data using classic scatter plots or bar charts and advanced charts that include heat maps, network graphs or sunbursts, and more.

As your company grows, so does your data. KNIME helps you build workflow prototypes and scale workflow performance through multi-threaded data processing and in-memory streaming.

The software is great for data scientists who want to integrate and process data for statistical models and machine learning but don’t have strong programming skills.

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4. RapidMiner

RapidMiner is a cloud-based suite of products that helps you create an integrated end-to-end analytics platform. The open-source product offers a wide range of features including automation, through which it loops and repeats tasks and can complete in-database processing automatically.

The software also offers real-time scoring, which lets you work with third-party software to apply statistical models. It operationalizes preprocessing, cluster, predictive, and transformation models.

If you want to delve deeper into your data, RapidMiner offers interactive visualizations like graphs and charts that you can get from the platform with zooming, panning, and other moderate drill-down capabilities.

Its drag and drop environment ensures that you have a unified environment in which you can create analytics workflows and develop predictive models.

You can also analyze over 40 data types, whether structured or unstructured like images, text, audio, video, social media, and NoSQL.

The platform uses a code-free interface, making it easier for you to design big data workflows and integrations.

The main advantages of using RapidMiner include the fact that its open-source, performs data prep and ETL in-database for best performance, and increased analytics speed. It also lets you build code-free workflows and tap into the most sophisticated analytics options like machine learning, AI, and predictive modeling for deeper insights and more business intelligence.

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5. RStudio

RStudio is not only an open-source data analytics tool but also an integrated development environment suite for the R coding language. The tool can create interactive reports, documents, web applications, and other types of reporting.

The software uses in-memory processing and can parse big data via connections and integrations. It’s capable of doing this thanks to the coding tools synthesized into RStudio for easier advanced processing of all your data.

If you want extra features though, you can go with the commercial format that includes more sophisticated security and collaboration efforts. The free version offers API connectivity, end-to-end analytics, visualization creation and distribution, and data ingestion.

You can deploy RStudio on your web browser through a connection to the RStudio server or as a standalone application.

With RStudio, you get streamlined R programming to execute code directly from the source editor. You also get to investigate trends on a big data scale, sophisticated ready-to-install R packages, and easily digest analyzed data through integrated visualizations and data consumption deployment vessels.

Other features that make RStudio worth considering include a source editor, web apps, and Flexdashboard for developing interactive dashboards. RStudio also provides integration with Apache Spark and RStudio Connect to help you publish your analyses in a visually impactful format.

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6. Apache Spark

Apache Spark is a unified, open-source analytics engine that introduced a new system for rapid and distributed large-scale data processing. The software runs pretty fast and you can download, modify, and redistribute it for free to use it as a standalone or integrate it into your workflow for processing needs.

Spark can process data in real-time, distributing it across clusters and using discretized streams to parse data into batches you can manage. Once the data is in manageable batches, you can organize and parse it out for speedy processing.

Plus, Spark offers a Cluster Manager that allows for increased control over clusters and you can quickly automate and process your data.

Spark also offers fault tolerance that helps protect users from crashes and recovers lost data and operator state automatically. This way, your resilient distributed datasets are able to recover from node failures.

Spark works with R, Java, Python, Scala, and SQL so you can integrate it into your mainstream big data workflow. You also get hundreds of prebuilt packages and API development support.

The software offers machine learning at a big data level, GraphX for graph-parallel computation and graph generation in the system, data streaming, and connection to virtually every mainstream data source.

However, security is defaulted to off, meaning your deployments are potentially vulnerable to attacks. Plus, backward compatibility doesn’t appear to be supported in newer versions and you have to set the caching algorithm manually.

Apache Spark also doesn’t offer traditional support for its products so you’d have to rely on the open-source community to answer questions and documentation. It’s in-memory processing also takes up a large chunk of memory.

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7. Pentaho

Pentaho is a data analytics platform that offers a suite of open source and proprietary tools. The tool also has a community edition with pared-down features but you can still access the source code via this package. This way, you can extract, transform, load, and create visualizations on an annual basis.

With Pentaho, you can gain insights to manage your data and drive business decisions. Among its notable features include dashboards, data visualization, data modeling, role-based security, reporting, mobile access, and a wide range of analytics capabilities.

Pentaho serves all sizes of businesses in all industries and can be used by anyone at any skill level. Plus, Pentaho can be delivered as an embedded implementation or in the cloud, and is built on open-source principles thereby leveraging existing and future data.

You get several benefits by using Pentaho including high-level overviews that enable you to capitalize on wins, track key performance indicator progress, and improve on stagnant growth. On top of that, its code-free design ensures that you can enjoy enhanced productivity.

Pentaho also integrates with Hadoop and Spark, which ensures that you can aggregate, prepare and integrate your big data, create interactive visualization, analysis, and prediction.

You can also blend multiple sources and process your data at a larger scale within a visual design environment.

You need not worry about data management because Pentaho is efficient enough to improve pipeline management for all your data. In addition, Pentaho helps engineers and analysts perform automated data integration tasks easily.

With Pentaho’s predictive analytics, you can monitor, evaluate, compare, and rebuild predictive models using machine learning algorithms to perform predictive analysis.

Pentaho also offers multi-cloud support, metadata editor, and community-driven tools to extend standard data analysis functions.

The main drawbacks of using Pentaho include unclear error codes, scheduling ETL packages through Task Scheduler in Windows is difficult, and sometimes the database connection information times out. Pentaho’s help documentation could also use some improvement and more guidelines for creating models manually or setting analytical diagrams would be nice.


BIRT is an open-source technology platform that you can use to create data visualizations and reports and then embed them into rich web or client applications.

The software project is supported by a large, active, and growing user community at Eclipse and the BIRT Development Center and is one of the most widely adopted technologies for data visualization and reporting.

The BIRT technology platform has a visual report designer and runtime component for creating BIRT designs and generating them for deployment to any Java environment.

Plus, the BIRT project includes a charting engine that’s fully integrated into the BIRT designer and you can use it as a standalone tool to integrate charts into apps.

BIRT designs can access a number of different data sources because they’re persisted as XML. Such data sources include JFire Scripting Objects, JDO datastores, POJOs, Web Services, SQL databases, and XML.

BIRT is big on open standards and integrates with data sources in any environment. Moreover, you can easily explore, visualize, and collaborate using your data in BIRT.

The tool facilitates report creation with an aim to meet your business intelligence needs. The charting engine allows you to embed fully integrated charts and reports into apps or into the report designer.

Among the benefits you can accrue from using BIRT include data blending, which allows you to incorporate data from multiple sources into a single source and get an overview of your data to uncover trends. You also get to prepare your reports for presentation using various visualization options in BIRT.

BIRT also allows you to share your reports via email directly to other users or embed them into other apps like client-rich or web apps that allow embedded code.

Other useful features include lists, charts, crosstabs for presenting data in two dimensions, letters and documents, compound reports, and multiple data sources for data blending.

9. Metabase

Metabase is a simple and quick way to get business analytics and intelligence to everyone on your team. The open-source and free tool allows you to ask questions about your data, which helps non-technical users to use a point-and-click interface to construct queries.

The tool works well for simple aggregations and filtering, but more technical users can use the raw SQL for their complex analyses.

Metabase brings data tools with simple and elegant products to the enterprise world of business intelligence. The open-source app installs in minutes and you can connect it to popularly used databases and even share to applications like Slack.

Anyone in the company can create dashboards or emails without having SQL knowledge or experience. This way, it’s easy to measure, analyze, and share data, plus deal with any complexities as they arise.

The tool has powerful functionalities and supports most of the data sources you want to connect to it.

Metabase also allows you to deploy on your own platform thanks to its open-source code so you can query your data and get answers in formats like detailed tables or bar graphs.

The software handles a lot of the details to allow you to focus on whatever you want to communicate through the visualizations you create. For simple and custom questions, the software guesses at the appropriate chart it will use to display the results.

Other features you get include rich beautiful dashboards with fullscreen and auto-refresh, SQL mode for data professionals and analysts, and the ability to create canonical metrics and segments.

You can also send data to email or Slack on a schedule and view your data using the MetaBot. In order to humanize the data, you can rename, annotate and hide fields, and get alerts to see any changes in your data.

10. Kibana

Kibana is a free and open-source data visualization and exploration software that can run on-premises or other deployments like Amazon EC2 or Elasticsearch Service.

The software is used for log and time-series analytics, operational intelligence, and application monitoring. It offers easy-to-use and powerful features like pie charts, histograms, heat maps, line graphs, and built-in geospatial support.

Plus, Kibana offers tight integration with Amazon Elasticsearch Service, which is a common search and analytics engine. This makes Kibana one of the default data visualization choices for the Elasticsearch engine.

Among the benefits you can enjoy when using Kibana include interactive and intuitive reports and charts you can use to interactively navigate huge amounts of log data. You can drag time windows dynamically, zoom in and out of data subsets, and drill down on reports to get actionable insights derived from your data.

Plus, you get powerful geospatial support and capabilities for seamless layering of information on top of your data and then visualize the results on different maps.

Kibana also offers pre-built filters and aggregations so you can run various analytics such as top-N queries, histograms, and trends in a few clicks. The dashboards are easy to set up and access or share with others especially if you have a browser to view and explore the data.

What To Look For In An Open Source Data Analytics Tool

Once you have your data, you need to consider your business needs and learn who will be using the tool in your organization like data scientists or analysts, or non-technical users who need an intuitive interface.

You can also find out whether the tool offers support for visualizations that are relevant to your company and whether it provides an interactive experience for iterating on code development.

Consider also the data modeling capabilities, for instance, some tools can perform data modeling while others support a semantic layer. You can also get a tool that uses SQL to model your data before analysis.

Finally, consider licensing and pricing because open-source doesn’t always mean free. In many cases, it means the source code is available and you can edit it as an end-user. You can copy, modify, or redistribute it depending on the creator’s license.

Make sure the software has an active community and collaborators who advance the software often and as far as possible, and that the software is customizable, cost-effective, and nonbinding.

Wrapping Up

Data analysis is at the core of every modern business.

However, when it comes to choosing the data analytics tools, it can be challenging because there’s no tool that fits every need.

These 10 open-source data analytics tools stood out as the best software on the market. While they may not fit the exact needs of your business, they still offer some of the main features you need to prioritize in business and then you can find the one that best suits your current needs.

About Author

Tom loves to write on technology, e-commerce & internet marketing. I started my first e-commerce company in college, designing and selling t-shirts for my campus bar crawl using print-on-demand. Having successfully established multiple 6 & 7-figure e-commerce businesses (in women’s fashion and hiking gear), I think I can share a tip or 2 to help you succeed.