The Best Open Source Big Data Tools Of 2024

As a buyer, did open-source analytics software feature in your product shortlist on the first pass? Maybe not. Did you know that 96% of scanned codebases use open-source components, and 76% of code is open-source?

Community-driven code programs are the backbone of software development, and you risk missing out on great functionality if you don’t consider them when buying data solutions.

This article showcases our analysts’ top five picks for best open-source big data analytics tools, along with software selection tips.

Compare Top Big Data Software Leaders

What This Article Covers

• Best Open-Source Big Data Tools
  • KNIME Analytics Platform
  • RapidMiner
  • RStudio
  • Spark
  • Pentaho
• Open-Source Software Benefits
• Software Selection Strategy
• Next Steps

Open-source software is a publicly available application code for viewing, modification and distribution.

While there’s significant overlap between free and open-source software, open-source programs aren’t always free, and not all free software is open-source.

Best Open-Source Big Data Tools

The best open-source analytics tools are end-to-end data management platforms with big data integration, ETL and data preparation. They form robust integrations and scale with increasing data volumes.

The interface is functional, though it might not be very intuitive. Many open-source platforms are cloud-based and provide AI (artificial intelligence) with the capacity to build ML models.

Many open-source tools don’t offer mobile support.

Compare Top Big Data Software Leaders

KNIME Analytics Platform

It’s an open-source end-to-end data analytics platform that integrates with third-party systems. You can host KNIME on-premise or with Microsoft Azure.

Interactive data views allow data exploration with bar charts, lines, ROC curves and scatter plots. Or you can extend visualization options with tools like Tableau and Power BI.

Automatic data caching and parallel execution on multi-core systems enable performance scaling.

Top Benefits

• Establish Brand Identity: Build client trust — remove the black box of machine learning workflows with explainable AI (XAI) components.
• Incorporate Text Analysis: Analyze text documents using ML algorithms. Load them in any format and enrich text data with entity recognition and tagging. Filter and manipulate terms as desired.
• Use Data Science: Train ML models for classification using AutoML. Automate data preparation and parameter optimization with cross-validation, scoring, evaluation and selection.
• Accelerate Analytics: Reuse and share workflow components by bundling common segments using JavaScript, Python and R scripting.
• Gain Community Support: Access over 14,000 data science solutions with the KNIME Community Hub. Upskill with self-paced and guided courses.

Primary Features

• Data Analytics: Access algorithms for classification, time series analysis, regression, deep analysis techniques, clustering and dimensionality reduction methods.
• Network Mining: Derive helpful information, including predictions, from pharmaceutical and social networks using a network analysis plugin.
• Visual Workflows: Train models by joining, partitioning and visualizing datasets with its intuitive, drag-and-drop graphical interface.
• Data Integration: Choose to migrate to the cloud or opt for in-database processing with connectivity to SQL Server, PostgreSQL, Snowflake and BigQuery.
• Data Preparation: Prepare datasets for ML analysis with normalization, transformation and missing value rectification. Aggregate, sort and filter data locally or in distributed environments.

Limitations

• Doesn’t support key driver analysis.
• Doesn’t offer mobile support.

Compare Top Big Data Software Leaders

RapidMiner

It’s a cloud-based open-source analytics tool with an open core, meaning its core infrastructure is available under a GNU Affero General Public License. Its AI Cloud offering complements other products.

Commercial editions extend this core under a business source license, offering extended functionality to paying customers. RapidMiner Studio is the vendor’s data end-to-end integration tool for building code-free workflows.

Top Benefits

• Keep Data Secure: Get the advantage of Apache — enforce authorization and security protocols while encrypting HDFS data.
• Assimilate Text Data: Create documents from datasets and vice-versa, and perform filtering, extraction, stemming and transformation.
• Integrate With Apache Hadoop: Run processes parallelly in the Hadoop server and RapidMiner Studio. RapidMiner Radoop combines Hadoop and Spark’s functionality.
• Process Data In-Database: Execute data preparation and ETL in cloud data repositories, including MySQL, PostgreSQL and Google BigQuery. Design no-code data retrieval workflows in Studio.
• Build Web Apps: Provide web interfaces to end users for viewing, exploring and manipulating data using AI apps. They trigger RapidMiner processes to update the results.

Primary Features

• Data Connectivity: Read data from over 40 file types with support for NoSQL databases, MongoDB and Cassandra. Integrate audio files, images and time series data.
• Reusable Processes: Design repeatable data preparation workflows to extract, join, filter and group data from various sources. Save, schedule and share them for reuse.
• Data Visualization: Use over 30 visualization types and statistical summaries to understand data patterns and trends.
• Graphic Workflow Designer: Use drag-and-drop actions, readymade templates and intelligent recommendations to build analytics workflows and forecasting models.
• ML Models: Build models with over 1500 algorithms and functions. Choose from automated, visual and code-based interfaces, and get results faster with prebuilt templates.

Limitations

• Doesn’t provide mobile support.
• Doesn’t have extensive third-party integration.

Compare Top Big Data Software Leaders

RStudio

It’s an integrated development environment for the R coding language. It lets you create interactive web applications, reports and other business documents. In-memory processing enables big data analysis.

A paid version is available, though the free version provides end-to-end analytics with API connectivity, data sourcing, visualization and publishing. You can deploy it as a standalone or via the RStudio Server.

Top Benefits

• Analyze Data Visually: Build data models and visualizations and work on data frames, vectors and functions using tidyverse, ggplot 2 and dplyr.
• Leverage Machine Learning: Gain the benefit of machine intelligence by connecting to the TensorFlow, Keras and Estimator APIs.
• Get Data Analysis-Ready: Map datasets to their structure and interpret them to produce summary statistics using functions and dedicated packages.
• Process Big Data: Work with realistic runtimes — compress and downsample data to a downloadable size while keeping it statistically viable. Process data chunks in parallel, serially or after recombining.

Primary Features

• RStudio Connect: Share R Markdown reports, dashboard plots and Jupyter Notebooks in one place. RStudio Connect is a publishing platform for Python and R scripts.
• Sparklyr: Process big data using local and remote Spark clusters and R with Sparklyr. Build and tune machine learning workflows on Spark within R using ML algorithms.
• Flexdashboard: Publish related data visualizations in groups using RStudio packages and the Flexdashboard. Present visualizations in sequence with contextual commentary via storyboard layouts.
• Job Launcher: Run Jupyter Notebook, RStudio Pro and VS Code sessions within your computing cluster software. The job launcher runs within batch processing and container orchestration platforms.
• Visual Markdown Editor: View real-time content changes and get support for technical writing tasks like citations, outline navigation and scientific and technical writing features.

Limitations

• Doesn’t provide mobile insights out of the box.
• Doesn’t support collaborative editing.

Compare Top Big Data Software Leaders

Apache Spark

Spark is quickly catching up to its sister product, Hadoop, in popularity. Both are game-changers in the free open-source software landscape. Hadoop is a big data file system, while Spark is the actual engine for analytics.

Released eight years later than Hadoop, Spark introduced a new distributed and rapid big data analytics system that runs hundreds of times quicker than Hadoop’s MapReduce.

Spark is completely free to download, modify and redistribute. And if you don’t use it standalone, there’s a strong chance you’ll integrate it into your workflow for processing needs.

Top Benefits

• Obtain Comprehensive Insights: Incorporate data science-driven insights with its ML library. Get the latest information by ingesting and processing continuous, live-streaming data.
• Accelerate Analytics: Perform fast computing with parallel processing and graph processing. Spark can process data in real time by distributing it across clusters, a considerable edge over Hadoop.
• Gain Multi-Language Support: Spark supports Java, Scala, Python and R and has a native query language, SparkSQL.
• Onboard Teams Smoothly: Train teams with extensive documentation on the Spark architecture, APIs and libraries. Troubleshoot issues with how-to tutorials and examples.
• Support Clusters: Work with Apache Mesos, YARN and Kubernetes. Automate data processing with Spark’s cluster manager. Deploying with Mesos allows at-scale partitioning of Spark instances.

Primary Features

• Fault Tolerance: Secure against crashes with out-of-the-box fault tolerance, automatically recovering lost data and operator state. Resilient distributed datasets (RDDs) can recover from node failures.
• Spark SQL: Query data in Spark programs using SQL or a DataFrame API that interfaces with Hive, Avro, Parquet, ORC, JSON and JDBC.
• SparkR: Use Spark from R with a user interface, SparkR, that supports distributed machine learning and data operations, including selection filtering, selection and aggregation.
• PySpark: Analyze and interact with live, large-scale data in a distributed environment using Python with a dedicated Spark API.
• Shared Variables: Spark supports two variable types — broadcast variables perform the caching of values in memory, and accumulators are counters and sums to which you can add values.

Limitations

• Doesn’t provide mobile support out of the box.
• Can be resource-intensive when processing large datasets in memory.
• Doesn’t have a file management system.

Compare Top Big Data Software Leaders

Pentaho

The Pentaho platform provides a suite of proprietary and open-source data analytics tools. Pentaho is open-source, but the enterprise edition isn’t free to purchase.

The open-source Pentaho Community Edition provides core data integration capabilities and is accessible for on-premise, cloud and mobile use.

Tools like Kettle, Weka and Mondrian are community-developed and integrated into Pentaho. Community forums and marketplaces give users a platform for collaboration and sharing.

Top Benefits

• Improve Decision-Making: Build, train, test and run ML models using Python-R integrations and machine learning libraries like Spark MLlib and Weka.
• Gain Built-In ETL: Capture data with ETL, thanks to Pentaho Data Integration. Cleanse and store it in a consistent format for end-user analysis and IoT insights.
• Secure Data: Maintain data integrity with third-party web and security frameworks. Pentaho integrates with Active Directory, CAS, LDAP, RDBMS and Integrated Microsoft Windows Authentication.
• Enhance Functionality: Benefit from the Pentaho Marketplace — acquire plugins and language packs for official projects and project maturity classification for community and customer projects.
• Work With Hadoop: Gain the benefit of Hadoop distributions and version updates with a dedicated abstraction layer.

Primary Features

• Report Designer: Create dashboards using a drag-and-drop visual designer, perform calculations, charting and report formatting, and choose layout templates and themes using its reporting wizard.
• OLAP: Work with and interpret complex data without creating and managing physical cubes. Create and test OLAP cube schemas with the Mondrian schema workbench.
• Metadata Editor: Build metadata models and domains, add a custom metadata layer to a data source, or create relational data sources for a production environment using the metadata editor.
• Analyzer: Generate scatter charts, heat grids and multi-chart visualizations, and drill down into data using a web-based drag-and-drop environment.
• CTools: Interpret data by producing dynamic dashboards using JavaScript, CSS and HTML with CTools, a community-contributed framework.

Limitations

• Doesn’t support mobile insights.
• Doesn’t provide comprehensive help documentation.

Compare Top Big Data Software Leaders

Open-Source Software Benefits

It seems too many cooks don’t always spoil the broth. Citizen developers endow open-source software with many advantages, including cost-effectiveness and frequent code revisions and feature updates.

Collaborate

Hundreds, maybe thousands of contributors, prop up many mainstream open-source software products.

In many cases, these contributors are software enthusiasts with a common goal of developing the software.

• Development of new features is quicker with people at hand to implement them, not just an internal development team that may have to prioritize other tasks.
• You’d be hard-pressed to find open-source software without an extensive support forum. Apache Spark has one on Stack Overflow.
• Many conversations on these forums center around advancing the software technologically, but quite a few focus on providing support and answering users’ questions.
• Some platforms have community-contributed plug-and-use components, even complete workflows, available for use with little-to-no modification.

Open-source data analytics tools allow users to collaborate, learn and advance together.

Read this article for information on open-source code licenses.

Customize

Access to the source code means businesses can tailor the software to specific user needs.

• Developers can add or delete code, removing unnecessary pieces that would bog down an entity’s limited resources.
• Users can even choose from different solutions, for instance, using components from the Apache constellation of products and embedding or integrating them into RStudio.
• Most open-source analytics tools, especially big data platforms, are built to connect with other applications and programs.

The complex process of ingesting large quantities of raw, unfiltered data and turning it into actionable information requires significant system flexibility for each project.

Open-source data analytics tools are built to integrate and play nice with other software.

Implement Cost-Effective Solutions

While open-source doesn’t necessarily mean free, it often means cost reduction. If an open-source license is free, users pay for only the auxiliary components instead of everything.

It’s affordable compared to the software license prices, which can be prohibitively expensive.

With open-source software, you can avoid vendor lock-in. Sometimes, things don’t work out, and it’s especially true in the analytics world.
With a high probability of failure, you wouldn’t want to be stuck with a subpar system perpetually.

A company can move on from a failed endeavor without much heartache with free, open-source licenses.

Crowd-Source Data Security

The jury’s still out on open-source software’s security limitations, so take this section seriously. However, defenders of open-source big data tools claim it’s quite secure.

There’s some reasoning behind the optimism. Open-source software comes with more transparency and (theoretically) more eyes on any potential vulnerabilities.

Open-source software means a dedicated collection of individuals who constantly monitor the code for security vulnerabilities and can rapidly deploy patches.

It’s in contrast to an IT team that must focus on other projects — the scope of an open-source community should ideally be broad enough to protect the code and its users from attack.

Compare Top Big Data Software Leaders

Software Selection Strategy

Software implementation success depends on the technology and how well it aligns with your business goals and integrates into your processes.

• Formulate a diverse team, including project managers, department leaders and technical champions to gather comprehensive requirements.
• Define clear business objectives, whether you want to improve efficiency, enhance the user experience or stay compliant.
• Future-proof your purchase by anticipating imminent needs and technological advancements. Seek scalability to avoid costly migrations in the future.
• Customization and application integration are non-negotiable.
• Assess the vendor’s reputation by reading online product reviews and discussing with industry peers.
• While checking for data management capabilities, ask about data retention and archival strategies.
• SOC and historical audit reports can verify if the software adheres to compliance regulations.
• Check if user permissions and roles match your organization’s hierarchy.
• This one’s essential — consider running pilot programs with a small user group.
• Earmark training resources and change management strategies before you deploy.
• Stay informed about industry trends and advancements. Read our BI trends article.
• Seek legal counsel to review contracts and protect your interests.

Get started with our nine-step process. Read about it in our Lean Selection Methodology article.

Compare Top Big Data Software Leaders

Next Steps

Open-source solutions offer a cost-effective way to accelerate analytics, provide timely and accurate data, and optimize query performance.

We understand it can be daunting to choose a suitable solution amidst the many available choices. We match you with software that aligns seamlessly with your unique business requirements.

Get our free software comparison report on the leading open-source analytics software. Or compare your preferred products by feature with a number-based ranking system based on comprehensive user reviews.

Do you agree with our list, and why or why not? Did our analysts miss or overlook your personal favorite? Have you had more success with a commercial or open-source product? Let us know in the comments below!