Best Open Source Big Data Analytics Software Tools For 2020

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Getting smarter is always a good thing. Making informed decisions and capitalizing on inefficiencies and opportunities have always been crucial components of getting ahead of the pack in commerce. In the golden age of information, that means big data analytics tools. In 2020 and beyond, the field has diffused enough to get to free and open source analytics.

Analyzing data, especially in a business intelligence context, has become a norm, so much so that it’s diffusing to the masses. Community-driven solutions are no longer just creeping into the marketplace, but are legitimate alternatives to proprietary ones, with thousands of users and contributors backing their infrastructure.

But is an open source big data analytics software correct for your business? What should you look for in one?

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Best Open Source Big Data Analytics Software Tools

In this article, we’ll try to answer those questions and give you our top five open source products right now, based on analysis by SelectHub’s market experts.

What is Open Source Software and What are its Benefits?

There is a common misperception that open source means free. While this is true in many, if not most, cases, it isn’t a direct synonym.

Open source software simply means that the source code is available and editable by the end-user. They are allowed to copy, modify and redistribute it as they see fit, depending on the license given by the creator.

So what makes them more appealing than a proprietary option?

Benefits of Open Source Big Data Analytics

Collaboration and Community

Many mainstream open source software products are propped up by hundreds, maybe thousands of contributors.

In many cases, these contributors are enthusiasts of the software, all with a common goal of advancing the software as far as possible. When a new feature is necessary or simply desired, there will be a line of people to implement it, not just an internal development team that may have to prioritize other tasks first. Some people lean on open source software, but open source software also leans on people.

You’d be hard-pressed to find an open source software without an extensive support forum, such as Apache Spark’s through Stack Overflow. Many conversations on these forums center around advancing the software technologically but more still focus on providing support and answering questions other users have.

Some software have plug-and-use components, or even complete workflows, developed by community members and available for use by others with little-to-no modification. Open source software is a doorway for users to collaborate, learn and advance together.

Customizable

Access to the source code means the software can be tailored to the specific needs of a user or business. Code can be added or deleted, removing unnecessary pieces that would bog down an entity’s limited resources.

Users can even pick and choose from different solutions. They can use components from the Apache constellation of products and embed or integrate them into RStudio.

Most open source analytics software systems, especially open source big data tools, are built for connectivity with other applications and programs. It’s an essential functionality in a big data workflow — if for no other reason than connecting to data sources. The complex process of ingesting large quantities of raw, unfiltered data and turning it into actionable information, requires significant flexibility from a system to get that done for each individual project and its needs. Open source solutions are built to be integrable and play nicely with other software.

Cost Effective and Nonbinding

While open source doesn’t necessarily mean free, it does often mean cost reduction. If an open source license is indeed free of charge, instead of paying for everything, users just pay for auxiliary components, not the software. Things like server and storage space, hardware, access to data processing clusters and others still exist. This isn’t insignificant, as some software licenses are prohibitively expensive to a small business.

But a huge monetary perk of open source software is avoiding vendor lock-in, or being stuck in a contract with a system. If we’re being honest, sometimes things don’t work out. This is especially true in the analytics world. Gartner predicts that through 2022, only a fifth of analytic insights will produce verifiable business benefits.

With failure a high probability, it makes sense that you’d want to not be stuck with a solution that is obviously not going to do what you need it to do. With free open source licenses, a company can move on from a failed endeavor with a smaller cost. This maneuverability lets companies get the most out of their analytics efforts by working with different systems and finding the one that best suits their needs, instead of making an educated guess beforehand and committing to one.

Data Security

The jury is still out on open source software’s security limitations, highlighted by the Equifax breach of 2018, so take this section with a grain of salt. But defenders of open source big data tools claim it is actually more secure than their proprietary alternatives.

There is some reasoning behind the optimism. Open source software comes with more transparency and (theoretically) more eyes on any potential vulnerabilities. Hopefully, open source software means a dedicated collection of individuals is constantly monitoring the code for weaknesses in security and able to deploy patches rapidly. This is in contrast to an IT team that might be bogged down with other projects — the scope of an open source community should ideally be broad enough to protect the code and its users from attack.

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Top Open Source Big Data Tools

KNIME Analytics Platform

The KNIME Analytics Platform is the epitome of an open source software. Its source code is readily available for download and can do end-to-end big data analytics out of the box. It is propped up by an extensive community of users, who design and share extensions, components and entire workflows for distributed use.

KNIME Analytics Platform

A KNIME Analytics Platform workflow

The key features that make KNIME one of the top open source analytics tools are:

KNIME Hub

The KNIME Hub is a repository for user-created assets, such as task nodes, extensions, connectors, layer components and complete stock workflows. These assets are free to upload and download, modify and use. It allows for increased collaboration not just within a project, but throughout the entire community.

Visual Workflows

A drag-and-drop interface allows workflows to be designed visually, rather than through coding. These workflows flatten the learning curve for advanced analytics, and easily interchangeable components make tweaking the system easy.
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In-Database or Distributed Processing

Flexible data processing capabilities allow for functions in-database. This means easier analytics and less preparation or distributed processing across a cluster, leading to scalable analytics at the big data level. KNIME Server, a side offering, also allows for increased data storage and management, but comes at a price.

Data Modeling

Advanced analytics allow for predictive and prescriptive data models to be created, tested and verified. It can use machine learning and explain the models using LIME and Shap/Shapley values. It uses performance metrics like R2 and ROC.

Analytics Scaling

Through integrations, distributed analytics and performance scaling via in-memory streaming and multi-threaded data processing, overall analytics can be scaled to big data levels. Users can analyze as much data as they can get their hands on.

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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.

RapidMiner

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.
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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.

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RStudio

RStudio is the only product on this list dedicated solely to the development of an open source data analytics software for the R coding language. It is an integrated development environment for one of the top data analysis coding languages in the world. It can create interactive web applications, reports, documents and other forms of reporting.

RStudio

An RStudio console showcasing code, data and resulting data plot

RStudio earns a place on this list due to these features:

R Language

No other data science program has a community dedicated to a single coding language like RStudio does. While it does offer support for Python, its community is dedicated to providing support for R and documentation to manage several working directories.

Source Editor

The source editor provides a synthesized view of all tools in use, including extensions, without leaving a singular window. The console marks syntax, define functions, complete code and other variables for ease of use.
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Package Manager

A repository enables offline access and automatic syncing to CRAN, and provides a series of self-developed R packages for each stage of a workflow, from ingestion to visualizations, ready to install. The repository allows for collaboration across teams and departments.

Web Applications

RStudio’s Shiny Server enables the development and production of web applications, either stand-alone or embedded into other web pages and platforms as dashboards or R markdown documents.

Dashboards and Interactive Graphs

Dashboards present related visualizations, with support for a variety of components such as HTML widgets. RStudio’s Shiny and ggvis R package allow for the creation of interactive graphs and reports that can be used to produce drill-down research. Dashboards and interactive graphs can be published to the web and updated in real-time.

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Apache Spark

Apache Spark is quickly catching up to its sister product Hadoop in popularity. Both are considered landmarks in the free open source software landscape — Hadoop is a big data file system while Spark is the actual engine for analytics. With an initial release eight years later than Hadoop, Spark introduced a new system for distributed and rapid big data analytics that runs up to 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 end up integrating it into your workflow for processing needs.

Apache Spark

A Spark job run summary

The reasons Spark was determined to be a top product are:

Speed

Spark can process data in real time, a huge edge over Hadoop. It distributes data across clusters and uses discretized stream, a form of high-level abstraction to parse flowing data into manageable batches that can be organized and parsed out for quicker processing.

Cluster Managers

It provides its own cluster manager or works with Apache Mesos, YARN or Kubernetes. This allows for increased control over clusters or the ability to automate and process more data quicker. Deploying with Mesos allows multiple Spark instances to be partitioned at scale.
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Fault Tolerance

With so much data going through such complex processes, things can go wrong quickly. Spark protects users from crashes with out-of-the-box fault tolerance, automatically recovering lost data and operator state. Resilient Distributed Datasets can recover from node failures.

Connectivity and APIs

Spark is compatible with Java, Scala, R and Python, and SQL, with API development support and hundreds of prebuilt packages for each. It can be integrated into most mainstream big data workflows, and can function standalone through connections with other big data components.

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Pentaho Platform

The Pentaho Platform provides a suite of both proprietary and open source data analytics tools. Pentaho is open source, but the enterprise edition is not free to purchase. Its community edition offers pared down features, but still grants access to the source code and allows for extract, transform and load and visualization creation, with two major releases annually.

Pentaho Platform

A Pentaho visualization

The following Pentaho features place it on this list:

Data Integration

Pentaho Kettle is the program for data integration. It performs ETL using a metadata-driven approach, helping it specialize in semi-structured data analysis. A drag-and-drop interface eases the difficulty of adding data to a system.

Community

Tools like Kettle, Weka and Mondrian are community developed and integrated into Pentaho, and have become essential pieces. Community forums and marketplaces give users a platform for collaboration and sharing.
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Data Lineage

Data can be tracked from end-to-end, giving users full transparency into the analytics process. Implemented third-party tools allow tracking and viewing of specific data points.

Reporting Engine and Designer

Pentaho’s advanced visualizations and tools make consumption streamlined. An embeddable Java library allows both client- and server-side reports to be developed. A desktop application designer creates a visual environment for designing reports.

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Conclusion

These five products stood out as the top general open source data analytics software on the market. But they might not fit the specific needs of your business. SelectHub’s requirements template can provide a more focused view of what features your business wants to prioritize. Then, our vendor comparison matrix can help you find which solution might work best for you.

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 at the bottom of this page.

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Richard AllenBest Open Source Big Data Analytics Software Tools For 2020

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