Everybody loves charts, graphs...visualizations! They are neat, fast, and straightforward. Even with messy and disorganized data, a good visualization is the key to show insights and features that are difficult to point out on a raw table. In this blog post I will show you how to build a simple, but useful and good-looking dashboard to present your data - in three simple steps!
Continuing with our series of articles about cloud connectivity, this blog post is an introduction of how to use KNIME on Databricks. It's written as a guide, showing you how to connect to a Databricks cluster within KNIME Analytics Platform, as well as looking at several ways to access data from Databricks and upload them back to Databricks.
By Michael Berthold (KNIME). As first published in InfoWorld.
With new Integrated Deployment extensions, data scientists can capture entire KNIME workflows for automatic deployment to production or reuse
I know you are still using Excel sheets to transform and/or analyze your data! I know, because most of us still use it to some extent. There is nothing wrong with using Excel. Excel spreadsheets are a great tool to collect and transform small amounts of data. However, when the game becomes harder and requires larger amounts of data, Excel starts showing its limitations.
All individuals are unique and so are our data needs. From simple csv files to REST APIs to Google’s BigQuery or using customized shared components, KNIME Analytics Platform offers many ways to access and analyze your data. Today, we will demonstrate how to access all of these aforementioned data sources through the use case of analyzing and annotating gene expression data.
The availability of scientific datasets in Google BigQuery opens new possibilities for the exploration and analysis of public life sciences data. Especially the Google Cloud Platform (GCP) provides a place where SQL queries can be easily and intuitively created in order to explore huge datasets extremely fast.
Author: Paolo Tamagnini (KNIME)
One of the key challenges of utilizing supervised machine learning for real world use cases is that most algorithms and models require lots of data with quite a few specific requirements.
