Creating Shared Context For Your Data Warehouse With A Controlled Vocabulary
Data Engineering Podcast
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The Evolution of the Vocabulary
In data modelling, we tend to break things up into more smaller unitary, modular components that can then be pieced back together when they need to be. It's easier to keep that consistency over time, while things flexible if you don't overload any one stub with too many different meanings. But i think probably some really good analogies to how we think about something like a dada fault, which can also keep your i scheme as flexible as possible. If you start tolament when you're too mature, there's a lot of baggage and back tracking required.
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Summary
Communication and shared context are the hardest part of any data system. In recent years the focus has been on data catalogs as the means for documenting data assets, but those introduce a secondary system of record in order to find the necessary information. In this episode Emily Riederer shares her work to create a controlled vocabulary for managing the semantic elements of the data managed by her team and encoding it in the schema definitions in her data warehouse. She also explains how she created the dbtplyr package to simplify the work of creating and enforcing your own controlled vocabularies.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Emily Riederer about defining and enforcing column contracts and controlled vocabularies for your data warehouse
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by discussing some of the anti-patterns that you have encountered in data warehouse naming conventions and how it relates to the modeling approach? (e.g. star/snowflake schema, data vault, etc.)
- What are some of the types of contracts that can, and should, be defined and enforced in data workflows?
- What are the boundaries where we should think about establishing those contracts?
- What is the utility of column and table names for defining and enforcing contracts in analytical work?
- What is the process for establishing contractual elements in a naming schema?
- Who should be involved in that design process?
- Who are the participants in the communication paths for column naming contracts?
- What are some examples of context and details that can’t be captured in column names?
- What are some options for managing that additional information and linking it to the naming contracts?
- Can you describe the work that you have done with dbtplyr to make name contracts a supported construct in dbt projects?
- How does dbtplyr help in the creation and enforcement of contracts in the development of dbt workflows
- How are you using dbtplyr in your own work?
- How do you handle the work of building transformations to make data comply with contracts?
- What are the supplemental systems/techniques/documentation to work with name contracts and how they are leveraged by downstream consumers?
- What are the most interesting, innovative, or unexpected ways that you have seen naming contracts and/or dbtplyr used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on dbtplyr?
- When is dbtplyr the wrong choice?
- What do you have planned for the future of dbtplyr?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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