If you are looking for best open source data analytics software, you have arrived at the right page. In this article, we’ll discuss a variety of open source options available to help you make a choice for your data analytics needs.
Don’t have the budget for proprietary data analytics software? Don’t want to be tied to any one company or technology? The best open source data analytics software can help solve your problem. Open source data analytics software is software that is freely available for use, modification, and redistribution. These are some of the most popular open source data analytics packages on the market today.
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.
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.
KNIME
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.
RapidMiner
The RapidMiner platform is a suite of cloud-based products to create an integrated platform for end-to-end analytics. It is, technically speaking, an open core product, meaning its core infrastructure is available under a GNU Affero General Public License. This means the broad range of offerings is limited to commercial pricing, but a pared-down version of RapidMiner Studio is available and distributable.
An example of a RapidMiner modeling workflow
RapidMiner makes the cut because of these features:
Automation
Process control operations allow for looping and repeating tasks. It can complete in-database processing automatically. Users can set this to occur on a schedule or triggered by actions. The extensions Turbo Prep and Auto Model give RapidMiner the ability to complete a data science workflow completely automatically. Integration with RapidMiner Server, its commercial offering, enables more automation features.
Real-Time Scoring
A scoring engine allows the application of models in both RapidMiner and third-party software. It operationalizes cluster, preprocessing, transformation and predictive models. REST API lets scoring agents reach external data and platforms.
Data Visualization
Interactive visualizations let users delve deeper into the data. Visualizations, like charts and graphs, can be produced from within the platform with moderate drill-down capability, such as zooming and panning. Plots can be exported and transferred to other applications.
Visual Workflow Designer
A drag-and-drop environment creates a unified environment for creating analytics workflows and developing predictive models. RapidMiner offers more than 1,500 stock algorithms and functions, with prebuilt templates. It uses an AI to make recommendations on next steps in building a flow, created based off other user activity.
Data Management and Access
Users can analyze more than 40 types of data, structured and unstructured. This includes text, images, video and audio, social media and NoSQL. It has wizards for scraping data from Microsoft Excel and Access.Price: $$$$$Deployment:
Platform:
Company Size Suitability: S M L
Countly for easy mobile analytics
Countly / Countly GitHub / AGPL v3 license / 4.6k stars
The strength of Countly is easy access to your data with read and write API access and analytics for mobile, web, and desktop. It features a number of open source plugins to help you collect and understand your data better.
The downside of the open source version is that it doesn’t include all the features of the Enterprise paid version. With open source, you miss out on real-time data, user profiles, and the ability to design funnels. The open source version also “stores data (only) in an aggregated format,” so you can’t export the data and perform more granular analysis elsewhere (though this does make reporting faster).
Conclusion
Data analytics software allows to create powerful reports, charts, and visualizations. Some of them are solely based on data while the others also work with R scripts. This guide lists the best open source data analytics software available for free for both Windows and Linux operating systems to analyze data, make sense of it, and produce meaningful results.