How Column-Aware Development Tooling Yields Better Data Models
Data Engineering Podcast
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The Impact of the Cloud on Data Modeling
The cloud has been the most impactful change in the ecosystem for probably the last decade if not longer. Large organizations have taken a bit of a longer time to do that because they have more existing infrastructure, they have bigger risk involved in actually making that migration. A lot of the patterns and practices around how to do data platforms and data warehousing in the cloud have been developed by smaller and faster moving organizations.
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Summary
Architectural decisions are all based on certain constraints and a desire to optimize for different outcomes. In data systems one of the core architectural exercises is data modeling, which can have significant impacts on what is and is not possible for downstream use cases. By incorporating column-level lineage in the data modeling process it encourages a more robust and well-informed design. In this episode Satish Jayanthi explores the benefits of incorporating column-aware tooling in the data modeling process.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Sign up free at dataengineeringpodcast.com/rudderstack-
- Your host is Tobias Macey and today I'm interviewing Satish Jayanthi about the practice and promise of building a column-aware data architecture through intentional modeling
Interview
- Introduction
- How did you get involved in the area of data management?
- How has the move to the cloud for data warehousing/data platforms influenced the practice of data modeling?
- There are ongoing conversations about the continued merits of dimensional modeling techniques in modern warehouses. What are the modeling practices that you have found to be most useful in large and complex data environments?
- Can you describe what you mean by the term column-aware in the context of data modeling/data architecture?
- What are the capabilities that need to be built into a tool for it to be effectively column-aware?
- What are some of the ways that tools like dbt miss the mark in managing large/complex transformation workloads?
- Column-awareness is obviously critical in the context of the warehouse. What are some of the ways that that information can be fed into other contexts? (e.g. ML, reverse ETL, etc.)
- What is the importance of embedding column-level lineage awareness into transformation tool vs. layering on top w/ dedicated lineage/metadata tooling?
- What are the most interesting, innovative, or unexpected ways that you have seen column-aware data modeling used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on building column-aware tooling?
- When is column-aware modeling the wrong choice?
- What are some additional resources that you recommend for individuals/teams who want to learn more about data modeling/column aware principles?
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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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Rudderstack:  RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines. RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team. RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again. Visit [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack) to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.
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