Snowflake Data Warehouse on AWS

Case Study Cloud

The Knot Worldwide builds a Data Warehouse on AWS to enhance data-driven decision making.

SITUATION

The Knot Worldwide wanted to design and build a centralized data platform and develop corresponding data ingestion services to ​perform key business analysis. During this initiative, the following challenges needed to be addressed:

  • The data transformation process needed to simplify the collection and analysis of semi-structured web, as well as mobile, traffic data (page views, clicks/taps and interactions)
  • Data needed to be processed and organized to serve a global audience 
  • The ingestion and processing of data needed to be readily responsive to an ever-changing business environment

SOLUTION

The Knot Worldwide was not only looking for a scalable future state solution, but also a partner to supplement their own internal efforts and help build and implement this data solution. Apex Systems has a long history of helping clients and partners with data and cloud transformation services. We worked together to build a cloud based data warehouse on Snowflake, leveraging AWS services. Some of the key components of this AWS solution are:

  • Migrate data transformation and processing into Matillion - a tool that natively supports structured and semi-structured data
  • Create a single source of truth for data analysis that is easy to scale on demand while meeting support and security requirements
  • Deploy Snowflake – a Cloud-based Data Warehouse – on AWS to consolidate enterprise data and support decision making

RESULTS

The Knot Worldwide architected, and with the help of Apex, built and deployed a modern, state of the art data analytics platform on AWS that allows information stakeholders to rapidly build and iterate dimensional models that feed business intelligence reports and visualizations. Additionally, the solution is able to leverage all the advantages of a Cloud-based environment. Finally, the solution data architecture is scalable, flexible, and extensible with the application of standards and best practices, robust data modeling, and high data quality standards.