You have created a data pipeline with KNIME Analytics Platform. But how to put it into production so as to make the data available to end users? In this course, we will show you how to use KNIME Software to test and deploy a data transformation workflow, automate its deployment and enable the subsequent data monitoring, and maintenance.
We will consider a use case of creating a data pipeline to manage the orders data for a restaurant franschise that receives data from various branches, demonstrate how to deploy the data transformation workflow manually or automatically, and how to schedule and trigger the execution of data pipelines in production environment.
In the first session of this course, you will learn how to prepare a data transformation workflow for deployment. In the second session, you will be introduced to KNIME Business Hub and will learn how to deploy a data pipeline as a scheduled or triggered execution. Next, in the third session, you will learn types of data pipeline - ETL and ELT, and how to use the Continuous Deployment for Data Science (CDDS) extension framework to enable automated deployment on KNIME Business Hub. Finally, in the fourth session, you will learn about the best practices to productionize data pipelines: the principles of data governance - quality, security and cataloging, orchestration and performance optimization.
This is an instructor-led course consisting of four, 75-minutes online sessions run by our KNIME data scientists. Each session has an exercise for you to complete at home and together, we will go through the solution at the start of the following session. The course concludes with a 15 to 30-minute wrap up session.
Session 1: Preparing a Data Pipeline for Deployment
Session 2: Introduction to KNIME Business Hub
Session 3: ETL and ELT; Data Pipelines Validation and Deployment Automation
Session 4: Best Practices when Productionizing Data Pipelines
Session 5: Optional follow-up Q&A (15-30min)