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.
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Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- 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
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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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- Great Expectations
- Controlled Vocabularies Presentation
- Data Vault