Case Study

B2B Software Company Modernizes Data Stack by Implementing Snowflake, Airflow, and dbt

The Customer’s Challenge

A leading B2B Software as a Service (Saas) client recently found itself with an ill-equipped technology stack after a period of rapid organizational growth. The client was left with a technology that often lagged, sometimes failed, and was overall cumbersome to the point that it affected customer satisfaction. Additionally, the client did not feel they could confidently make data-driven business decisions using their current data warehouse infrastructure.

phData’s Solution

phData partnered with the customer to migrate their existing data platform to the Snowflake Data Cloud and retire Redshift, a redundant extract, transform, and loading (ETL) solution in favor of a streamlined set of tools that support software engineering principles and best practices. The Astronomer version of Airflow was also included in the migration for orchestrated data, as was dbt Cloud for the transformational layer.

The Full Story

When a B2B SaaS company found itself in a period of hyper-growth, it quickly became clear that its current stack of technologies was no longer meeting its needs. As workloads increased, current systems became overloaded, crashed, and lagged resulting in slower-than-desired response times. Since some of the client’s data in their existing data warehouse, Redshift, was customer-facing, these system inadequacies began negatively impacting their client service agreements.

To solve this problem, the company’s leadership team came together to inspect the current data platform and gaps regarding organizational maturity. Shortly after, they engaged phData to begin work on a data strategy that would evolve their capabilities and make them more agile and insights-driven.

Why phData?

The client already had a trusting relationship with phData but also knew their stellar reputation with Snowflake and strong roots in data engineering. The client’s goal was to work with experts to become experts, themselves.

Modernizing tech stacks

Together with phData, the client worked to identify the pieces and parts that needed to be in place to handle both the technical and operational sides of the business. The first piece was finding a replacement for Redshift.

Snowflake was identified as an ideal replacement due to its computing power, advanced analytics, and consumption-based pricing. Additionally, during a series of corporate acquisitions, the client realized the benefits of data sharing through Snowflake. The technology would help them find common workbenches of tools for the orchestration and transformation of data throughout the acquisition process.

Next up, the Astronomer version of Airflow was chosen to orchestrate tasks across the stack because of its open-source heritage and extensive user community.

Lastly, dbt Cloud would handle the transformation layer, helping the client separate their development and production environments as well as reduce cycle times, better control code versioning, utilize a peer review system, and troubleshoot issues. 

With a solid data strategy in place and tools selected, phData got to work on the migration, working closely with the client, and sharing best practices along the way. The migration effort involved building interfaces to numerous complex APIs and refactoring numerous scripts into a robust data acquisition framework. In under a year, phData completed the entire migration and onboarding process successfully.

The client is now able to bring data from various data sources – namely their CRM and other ancillary systems – into Snowflake and extract all the relevant customer and prospect touchpoints. 

Not only can the client utilize their tech stack to enrich this data dynamically at a demographic and technographic level, but they can apply lead scoring to ensure proper prioritization of incoming leads. 


The client has truly taken their data strategy beyond vanity metrics into dollars and cents, by integrating Snowflake, Airflow, and dbt. They have quadrupled their data engineering team through onboarding efficiencies and process improvements. 

With a deeper bench and streamlined operations, the client’s data engineers can be more responsive to the business, resulting in improved customer satisfaction. Retiring old, insufficient, and expensive legacy software with a modern technology stack has provided the client with the confidence to use data to drive business decisions.

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