Scalable Snowflake data warehouse powers efficient pipelines and automated infrastructure

Wonderdynamics offers an innovative AI-powered tool called Wonder Studio for CG animation and visual effects (VFX).

It automates tasks such as animation, lighting, and composition of CG characters within live-action scenes, revolutionizing filmmaking and storytelling.

At Scaletech, we implemented a robust Terraform repository for Wonder Dynamics Snowflake data warehouse and provided strategic advice on data flow optimization.

Client: Wonder Dynamics
Year: 2024

Our client sought expertise in designing efficient data pipelines and implementing Infrastructure as Code (IaC) practices.

To support their cutting-edge AI and VFX platform, Wonderdynamics needed to optimize their data flows and implement a scalable, maintainable infrastructure for their Snowflake data warehouse.

Challenge

We conducted a thorough assessment of existing data flows and infrastructure to identify inefficiencies and areas for improvement.

Leveraging our expertise, we designed and implemented optimized data pipelines that enhance efficiency and scalability, ensuring seamless data integration and processing across platforms.

To support infrastructure automation, we developed a comprehensive Terraform repository for managing Snowflake resources, following Infrastructure as Code (IaC) best practices. This included implementing versioning, modularity, and reusability to improve maintainability and collaboration. Additionally, we provided extensive documentation and knowledge transfer to ensure long-term sustainability and ease of management for future teams.

Solution

Partnership outcomes

Through optimized data flows, Scaletech streamlined data operations, ensuring faster processing and seamless integration across systems.

By implementing Infrastructure as Code (IaC), we enhanced infrastructure reliability, reducing downtime and operational risks while creating a more stable and scalable environment.

This approach also improved deployment consistency, minimizing manual configuration errors and ensuring repeatable, efficient workflows. Automated infrastructure provisioning accelerated development cycles, while version-controlled infrastructure code fostered better team collaboration, enabling seamless updates and long-term maintainability.

60%

Faster data processing through optimized Snowflake data pipelines and streamlined data flow design.

70%

Reduction in manual configuration with fully automated, version-controlled infrastructure using Terraform and IaC best practices.

45%

Lower infrastructure and operational costs by streamlining deployments, improving resource utilization, and reducing downtime.

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