7 Best Practices for Building Data Applications on Snowflake

January 10, 2020

To be competitive in today’s data applications market, startups and independent software vendors must deliver products that ingest and analyze large volumes of data quickly and easily. These requirements hold true for all data app types, including business intelligence (BI), Internet of Things (IoT), marketing and sales automation, customer relationship management (CRM), and machine learning, to name a few.

Once they have met these fundamental needs, data app builders must also demonstrate their product’s strong performance for a large number of concurrent users on a global scale, all while keeping expenses in check, growing the business, and future-proofing their technology investments.

Our ebook, 7 Best Practices for Building Data Applications on Snowflake, explains how data apps, and the customers they serve, benefit from development on a cloud-built data platform, and it provides seven best practices around architectural, deployment, and operational settings, including how to:

  • Select virtual warehouse sizes strategically, by service or feature
  • Adjust minimum and maximum cluster numbers to match expected workloads
  • Target workloads to the right services, and more.
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How to Build Successful Data Applications on Snowflake
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