KNIME® provides performance extensions such as the KNIME Big Data Connectors for executing Hive queries on Hadoop, or the KNIME Spark Executors for training models on Hadoop using Spark. But sometimes it doesn’t make sense to run your analytics on a Big Data cluster. Recently we released the KNIME Cloud Analytics Platform for Azure, which allows you to execute your KNIME workflows on demand, on an Azure VM with up to 448GB RAM and 32 cores which is one easy way to boost the performance of some of your workflows.
However, there are still a few extra tips and tricks that you can use to speed up execution of your workflows.
KNIME Analytics Platform follows a graphical programming paradigm, which, in addition to clearly expressing the steps taken to process your data, also allows rapid prototyping of ideas. The benefit of rapid prototyping is that you can quickly test an idea and prove the business value of that idea practically. Once you’ve shown that business value, you may deploy the workflow to run regularly on KNIME Server. Below I’ve highlighted some of the tips and tricks I’ve learned from KNIMErs that might help speed up the execution of some of your workflows. Read on to learn more.