Learn how to scale analytics and AI with data processing automation.
Analytics and AI teams look to automate their data pipelines to scale. DataOps is the set of best practices and standards to automate, improve data quality, and govern data processing. This course provides an overview of DataOps and how to leverage best practices to continuously improve data processing automation and quality.
Scaling and Collaboration - How to grow the Data Engineering function?
Modern Data Practices - How to evolve your capabilities with emerging technologies and processes.
Continuous Improvement - How to design data pipelines to evolve with changing business needs and data.
Understand the processes, roles, and challenges of managing data pipelines.
Architect and implement data pipelines, including continuous integration and deployment of the pipelines.
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Improve data quality using test case management, automated testing, and data monitoring.
Automate data processing through architecting, designing, and promoting data pipelines.
Improve data quality with test design, automation, and data monitoring.
Improve visibility and governance with data lineage, meta-data, and data engineering standards.
This enables our tailored training experience. We want to make sure each class is right for you, and that you will get the most value out of completing it.
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I felt like we hit our stride and were sharing organizational context and prior experiences alongside course concepts in a way that elevated the training. I really enjoyed our discussions.
