Snowflake’s Best Practices for Data Warehouse Development


By Mike Sargo

Chief Data and Analytics Officer and Co-Founder of Data Ideology  

By implementing Snowflake's best practices, it becomes possible to better understand and articulate your data warehouse's overall structure and layout.

In order to be on the same page, you can plan, make it agile, automate your processes, and train your staff. We recommend the following  practices to implement with Snowflake for data warehouse development and optimization: 

Data Model 

The data model is an abstract depiction  of the data warehouse which is  represented by organizing data elements and documenting how they relate to one another. It's a communication tool that documents and shares the data integrated and managed in the data warehouse. To be successful with this tool, you need to:

  • Understand your organization's data needs. 
  • Design the data model out to facilitate communication with the business and how it supports their business processes.
  • Get on the same page with other teams for delineation and definition before development.
  • Document context, content, and sources. 

A data model prevents confusion and misunderstanding. With a source-agnostic integration layer, the data model supports the analysis of data sets and a more future proof data warehouse.

Data Flow Diagram 

The data flow diagram offers a visual representation of the repository design and data lineage. It’s important to:

  • Understand how it works.
  • Know  where the data is sourced, where it has been, and how it is transformed throughout the data flow lifecycle.
  • Implement performance enhancements effectively.

Ultimately, the data flow diagram is an essential tool for making changes to future data flow. 

Data Warehouse Automation 

Warehouse automation tools like AnalytixDS, Wherescape, and Ajilius offer faster ramp-up capabilities. The goal is to deliver data quickly  with a higher value and more informed business decisions. This can be accomplished by:

  • Launching  projects quickly and easily.
  • Leveraging IT resources while enforcing code standards.

It's more flexible to support more complex data integration requirements. The code is available right away for deployment for validation and testing in the virtual data warehouse. 

Automation is a crucial feature of Snowflake's best practices and standard operating processes. 

Make it Agile 

Snowflake once again proves that a cloud-based solution is the best option for most virtual data warehouse requirements:

  • It doesn't have to be a substantial monolithic data warehouse to be effective and efficient. 
  • The considerable size of a data warehouse is a drawback in many situations. 
  • The agile data warehouse methodology demonstrates the importance of planning and careful implementation to achieve the desired results.  

The agile framework allows you to quickly evolve as its priorities and needs and the data platform change.  

Snowflake Training 

While Snowflake is designed to be user-friendly and easy to understand, it's still important to train all employees in processes, procedures, and best practices.  Keep the following points in mind:

  • Snowflake is a cutting-edge technology with new approaches to loading data, storing data, scaling out, and sharing data. 
  • Any team  will be most effective with an in-depth understanding and knowledge of how it works.  

Snowflake training should not be an afterthought. It should be part of the initial onboarding to achieve optimal performance.  

How Data Ideology Supports Snowflake Best Practices 

Snowflake's best processes support fast, flexible, automated data processes and use that can be integrated and transformed as needed. 

At Data Ideology, we will work with you to determine the best practices and how to implement them as part of your data engineering process with Snowflake's data platform. Then, we help you work smarter, not harder.

Written by Mike Sargo
Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

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