KNIME in a nutshell
KNIME is a low-code data analytics platform known for its ease of use and versatility. Let's go through its most prominent features:
- KNIME allows the visual design of data analytics: this means that you can build your sequence of transformation and modeling steps by just drawing it. In the same way as you would sketch a flowchart to describe a process using pencil and paper, with KNIME you will use a mouse and keyboard to depict what you want to do with your data. This is the fundamental difference versus the approach implemented in code-based analytics environments: using tools like KNIME means you don't need to write a line of code unless you want to. The visual approach will also let you have a clear line of sight of what's happening with your data at each step of the process. This makes even complex procedures intuitive to understand and easier to build. For advanced data practitioners like data scientists, this means saving a lot of time for debugging a prototype, as they can easily spot issues along the way. For business users in need of some data analytics, KNIME offers a very hospitable environment, accessible to everyone who wants to learn from scratch.
- It is open source and free to use: you can download its full version and install it on your computer at no cost. Different from what happens with the trial version of other products, it offers the complete set of functionalities for data analytics without limitations or time constraints. For the sake of completeness: KNIME also offers a commercial product (called KNIME Server) that enables the full operationalization of workflows as real-time applications and services, but we will not need to use any of this on our journey.
- It offers a rich library of additional packages for extending its base functionalities. These are available—in most cases—for free. Some of these extensions will let you connect KNIME with cloud platforms (like Amazon Web Services or Microsoft Azure), access other applications (Twitter or Google Analytics, to mention a few), or run specific types of advanced analytics (such as text mining or deep learning). Some packages will even let you add some Python or R code into KNIME so that you can implement even the most specific and sophisticated functionalities offered within their extensive set of libraries. This means that if you know how to program, you can leverage that as well in KNIME. The good news is that—in the vast majority of cases—you simply don't need to!
- Lastly, there is a broad and growing community of KNIME practitioners around the world. This makes it easier to find blogs and forums filled with examples (like the KNIME official one, forum.knime.com), tutorials, and answers to the most frequent questions you will encounter. Generous KNIME users can also share some ready-to-use modules with the rest of the community to enable others to replicate them: this further enriches the functionalities available out there at the time of need.
All these features make KNIME an all-inclusive tool, to the point that some have called it the Swiss Army knife of data analytics. Whatever nickname we prefer to give it, KNIME is well suited for learning and practicing everyday analytics and is certainly a tool worth adding to our kit.
It's time to get KNIME up and running on your computer: you can download it from the official website www.knime.com. Just go to the Download page and get the installation started for your operating system (KNIME is available for Windows, Unix, and Mac). When you are done with the installation, open the app. At the first run, you might be asked to confirm the location of the Workspace; this will be the folder where all your projects will be saved. After confirming the workspace folder (you can select any location you like), you are ready to go: the KNIME interface will be there to welcome you.