The perennial question of data warehousing is how to model the information that you are storing. This has given rise to methods as varied as star and snowflake schemas, data vault modeling, and wide tables. The challenge with many of those approaches is that they are optimized for answering known questions but brittle and cumbersome when exploring unknowns. In this episode Ahmed Elsamadisi shares his journey to find a more flexible and universal data model in the form of the "activity schema" that is powering the Narrator platform, and how it has allowed his customers to perform self-service exploration of their business domains without being blocked by schema evolution in the data warehouse. This is a fascinating exploration of what can be done when you challenge your assumptions about what is possible.
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Your host is Tobias Macey and today I’m interviewing Ahmed Elsamadisi about Narrator, a platform to enable anyone to go from question to data-driven decision in minutes
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Narrator is and the story behind it?
What are the challenges that you have seen organizations encounter when attempting to make analytics a self-serve capability?
What are the use cases that you are focused on?
How does Narrator fit within the data workflows of an organization?
How is the Narrator platform implemented?
How has the design and focus of the technology evolved since you first started working on Narrator?
The core element of the analyses that you are building is the "activity schema". Can you describe the design process that led you to that format?
What are the challenges that are posed by more widely used modeling techniques such as star/snowflake or data vault?
How does the activity schema address those challenges?
What are the performance characteristics of deriving models from an activity schema/timeseries table?
For someone who wants to use Narrator, what is involved in transforming their data to map into the activity schema?
Can you talk through the domain modeling that needs to happen when determining what entities and actions to capture?
What are the most interesting, innovative, or unexpected ways that you have seen Narrator used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Narrator?
When is Narrator the wrong choice?
What do you have planned for the future of Narrator?
