As part of this client’s roadmap, they sought to enhance reporting and machine-learning capabilities and needed to accelerate delivery of data products. Due to previous success with maturing DevOps capabilities, they turned to Trility to help accelerate speed to market by automating data loading to the cloud-based data warehouse platform.
Trility previously helped this client meet the future demands of the global supply chain by maturing DevOps practices, achieving accelerated development, and automating critical workflows.
As part of the client’s roadmap, they sought to add several data products to enhance reporting and machine-learning capabilities. To achieve this goal, they turned to Trility to identify how they could accelerate having the necessary data products in production with DevOps practices.
Similar to the previous project, the Trility team mapped out the business processes, handoffs, and affected areas of business to identify bottlenecks. The team recommended automating data loads to the cloud-based warehouse platform in order to accelerate how quickly development could produce new data products.
To ensure stakeholder alignment, the team validated the current state and desired future state while documenting the workflow and responsibilities.
By automating Data Definition Language (DDL) / Data Manipulation Language (DML) flows to Snowflake, the client eliminated manual processes and automated access to secure, reliable data and data services across the enterprise.
Due to the narrow scope and timeframe, Trility provided recommendations for enhancements and established baseline KPIs to support long-term continuous improvement goals.
Some key recommendations for future improvements included enhancing workflows around identity and role management.
The client accelerated how quickly data products came online for use across the enterprise by one month by reducing tasks by 30 percent and manual handoffs by 50 percent. By automating loading data to the cloud-based data warehouse platform, the development teams had a scalable intuitive process to meet market demands.
Some technical outcomes included:
Automating Data Product GitHub repository
Standardizing Data Product YAML inputs into a user-friendly form
Snowflake schemas automated provisioning
Eliminating dev promotion dependencies
Automating workflow notifications
Establishing KPIs and capturing historical data for existing data products
Increasing transparency and auditability for the deployment process
