Becoming a data driven organization, supporting data science initiatives, or running an array of data driven applications need a coherent data platform approach to be successful. If ignored or done wrongly, data driven programs fail, falter or lag unnecessarily in the transition from working data science code to a production system that delivers reliable business value. Slow development speed, bad data quality, high maintenance toil, compliance-as-afterthought, security vulnerabilities, scaling pains and service downtime; preventable problems that can turn a good idea into an expensive failure.
We have compressed years of experience in successfully designing, building and operating large data intensive systems at a large variety of organizations into a structured approach to data architecture.
This structured approach to data architecture puts the delivery of business value first and allows you to make clear choices in design trade-offs before they become a problem.
Our course “Introduction to Data Architecture” teaches you that structured approach. You will learn:
The basic building blocks of data architectures
Common data architecture patterns and their trade-offs
The 7 core principles of Data Architecture and how to apply them
How to guard value and increase iteration speed using the process and deliverables of the Data Architecture Journey
How to formulate a high-level implementation plan based on business needs
How to identify and analyze the gap between the business needs and the existing data landscape
To apply this approach in workshops based on real-world cases
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