KNIME Best Practices for Data Engineering

This course focuses on using the KNIME Analytics Platform for data engineering and applying best practices in building data processing pipelines.

Learn the concepts behind connecting to multiple data sources, data anonymization methods, and advanced database topics. Get an introduction to the Apache Hadoop ecosystem and learn how to process big data with Apache Spark integration. Finally, you will learn how to build and orchestrate modular workflows.</p

Put your knowledge into practice with hands-on exercises to build and orchestrate two applications: First, extract, validate, transform, merge, anonymize, and load customer data into a database; second, use Spark to access, impute missing values, and aggregate website usage data.

• ETL vs. ELT, ETL: Extraction
• ETL: transformation and loading, data anonymization
• ELT, Big Data, Hadoop, Spark, Cloud
• Orchestration of data pipelines, data governance
• Q&A

Please note: This course consists of four 75-minute online sessions conducted by a KNIME data scientist. Each session includes an exercise that you can complete at home. The course ends with a 15 to 30-minute final session.

This course does not provide a detailed introduction to the KNIME Analytics Platform. You should be familiar with the KNIME Analytics Platform. We assume that you have already created KNIME workflows and are familiar with data processing concepts and techniques. We recommend attending this course after you have acquired KNIME L1 and L2 knowledge or an equivalent qualification.

You should already have the latest version of the KNIME Analytics Platform installed on your laptop, which you can download here: knime.com/downloads.