Build Data Products With Snowflake | Part 1: Leveraging Existing Stacks

Before diving in, we want to share a small achievement with you all 🗞️We’re thrilled to see more of you and have heard you loud and clear! Check out the milestone post on LinkedIn!With this new milestone, we are kickstarting a new series that will explore the practical implementation of data products with your existing toolset! We cannot thank you enough for helping us build this amazing community on product thinking and practices for data, and we hope to have your continued support! If you haven’t joined us already, we’d love to have you on board!Now, let’s roll up our sleeves! 🤺Building Data Products isn’t rocket science. While many conversations, articles, podcasts, and strategies distributed all over the data space might make you think otherwise, it’s mostly the result of information overwhelm, clashing viewpoints and approaches, and complexity that most technologists aren’t able to convey well.In this series, we want to highlight the ease of leveraging your existing stack to get going with Data Products. This piece is ideally for data leaders who want to adopt the data product approach while staying rooted in big investments like Snowflake, Dbt, Databricks, or Tableau.We’ll kick this off with a favourite: Snowflake! What’s the Motivation for Data Products?Every organisation and business function is driven through three fundamental motivations:Saving CostsIncreasing Revenue & ROIEnhancing experience for all (customers and business operatives)How does a data product approach play out to boost the above?Within the scope of this piece, we’ll primarily cover:✅ the cost optimisation angle✅ kickstarter: Integrating Snowflake and using it to activate data products ✅ and sprinkle a little of the rest📝 Note: In the upcoming pieces, we’ll cover the revenue and experience angle in greater detail.