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Snowflake's Comprehensive Data Stack Development with Fivetran
Technology Category
- Analytics & Modeling - Machine Learning
- Sensors - Airflow Sensors
Applicable Industries
- Equipment & Machinery
Applicable Functions
- Sales & Marketing
Use Cases
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
Snowflake, a leading data cloud company, was looking to centralize its data within the organization's Snowflake instance, ‘Snowhouse,’ to power segmentation models, recommendation engines, and ultimately build a 360-degree view of customers. The marketing intelligence team at Snowflake had a bold vision to predict real-time ROI to dynamically optimize all Snowflake marketing programs, disrupting legacy B2B marketing analytics practices, and create huge efficiencies. However, the company faced challenges in breaking down data silos and enabling efficient analytics. Snowflake used to keep its data modeling and transformation logic within a separate BI tool, which was time-consuming and prone to error. Every time the business needed to run models out of the tool, or conduct ad-hoc analytics, analysts needed to recreate their models from scratch.
About The Customer
Founded in 2012, Snowflake has become ubiquitous with the growing modern data stack ecosystem, delivering access to the data cloud for over 6,000 customers worldwide. Snowflake’s own growth has been driven by rapid iteration and best-in-class data practices, including the development of a powerful internal data stack that gives every employee at the organization access to data. At the core of this meteoric rise is Snowflake’s marketing intelligence function, a team with a bold vision: to predict real-time ROI to dynamically optimize all Snowflake marketing programs, disrupting legacy B2B marketing analytics practices, and create huge efficiencies.
The Solution
Snowflake partnered with Fivetran to build a comprehensive data stack. Fivetran, a critical part of the modern data stack for Snowflake, provided over 900 connectors moving over 400 million monthly active rows, both through marketing analytics connectors such as Google, Bing and Facebook Ads, and core SaaS tools like Marketo, Salesforce and Jira. The implementation of dbt Core™ enabled a much more flexible experience for end users within the business. The team saw better overall performance as most of the compute-intensive calculations were conducted earlier in the process. Snowflake also used Airflow to manage dbt™ jobs and define however often they want to update their models and tables, giving analysts the ability to balance data refresh times with performance and cost considerations. The company established a data consumption layer where the marketing analytics team enables collaboration and invites its business partners to start their interactions with the data.
Operational Impact
Quantitative Benefit
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