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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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?
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.
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.
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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