Since March 2019 over 9,000 workflows and 750 components have been uploaded and shared publicly by the community. KNIME Hub, a crucial part of our open ecosystem, is an easily accessible repository for data workers to find and share solutions and collaborate on data science work.
This month, KNIME Hub is celebrating its third birthday.
Data workers go to the hub so they don’t have to start from scratch when they’re working on a given problem. Or, if they don’t even know where to start, they can browse by their industry (search “Manufacturing”), by their technique (search “Sentiment Analysis”), or by their use case (search “Churn Prediction”).
“The KNIME Hub is great for idea generation or to help you get unstuck,” says Tosin Adekanye, an active community member and published author, in her recent blog article. In a sentiment analysis project, she was pulling tweets and then ran into a problem with emojis contained in the tweets. She found help in the form of a String Emoji Filter component shared by KNIME community member, takbb.
As a knowledge-sharing space, the KNIME Hub makes it easy for the community to access, collaborate, and share solutions. It’s a great resource for community members to go and get help on any topic.
The community is uploading and publicly sharing around 60 workflows per week that anyone can access and download for their own projects. And interaction on the hub is growing. In the last 3 months, the average number of workflows uploaded to the hub per week is at approx.180! Search the hub for help with anything from financial analysis and guided analytics through to text processing, time series analysis, or automation.
“With a simple drag-and-drop, my workflow now has an online home where it can be easily found and installed by fellow KNIME users,” says Angus Veitch, Data Analytics Consultant (Forest Grove). Forum members upload workflows to the hub, providing quick help to answers like this one on how to use SHAP to explain decision tree prediction.
Tip: You can also search the KNIME Hub directly from the Forum post-editor to quickly insert links into questions/answers
Community members also make use of their private spaces on the hub for example, to download course exercises, data and solutions, which Ángel Molina Laguna, Data Analyst in Finance and Administration, did when he was learning KNIME. They can work on solutions either privately or make the space a public collaboration space and invite contributors to work together.
Easy knowledge-sharing and collaboration on data science projects
The ability to work together easily is key to ensuring successful data science projects. If you haven’t already, start exploring the KNIME Hub for solutions and easy collaboration.