Optimizing Snowflake Foundations for Scalable Growth

About the Client:

The client is a premier operator in the global gaming and casino industry, overseeing a vast portfolio that includes brick-and-mortar casinos, online gaming platforms, and luxury hospitality venues.

Background:

The client adopted Snowflake to support 24/7 analytics and real-time insights. As usage expanded rapidly, performance bottlenecks, rising costs, inconsistent access controls, and governance gaps began to emerge—posing risks to agility and scalability.

Challenge:

As Snowflake became the cornerstone of the client’s data infrastructure, several challenges emerged:

  • Inefficient Warehouse Sizing: Over- or under-provisioned warehouses led to poor performance and inflated costs.
  • Manual Access Management: Role assignments lacked consistency and scalability, leading to risk and overhead.
  • Fragmented Schema Design: Inconsistent, team-specific schemas created duplication and analysis hurdles.
  • Lack of governance controls: Difficulty to enforce consistent policies or meet audit requirements in a regulated environment.

Solution:

Our team partnered with the client to launch a phased, full-scale optimization of their Snowflake ecosystem.

Phase 1: Warehouse Optimization

  • Workload-Based Sizing: Used Snowflake’s QUERY_HISTORY and WAREHOUSE_METERING_HISTORY to profile workload patterns. Warehouses were resized based on usage types—small for ad-hoc queries, medium for reporting, and large for data transformation jobs.
  • Set aggressive suspend times (60s–5min) to cut idle credit consumption.
  • Multi-Cluster Warehouses: Enabled elastic scaling for real-time dashboards and high-concurrency use during peak hours.
  • Resource Monitors: Applied credit limits per warehouse to control spend and prevent overruns.

Phase 2: Streamlined User and Access Management (RBAC)

  • Hierarchical RBAC Design: Mapped organizational roles to Snowflake permissions (e.g., MARKETING_READ_ONLY, DATA_ENGINEER_WRITE, EXECUTIVE_DASHBOARD) and implemented them using RBAC model.
  • Applied principle of least privilege—granting only the required permissions to each role to reduce exposure.
  • Proposed integration with Identity Provider (IdP) for automated provisioning/de-provisioning of users and roles.

Phase 3: Schema Design Standardization

  • Medallion Architecture Adoption: Introduced a layered architecture—Bronze (raw), Silver (cleaned), Gold (business-ready)—to standardize data lifecycle across all domains (slots, poker, sports betting, etc.).

  • Defined standard naming & documentation to support data discoverability.
  • Best Practices for Modeling: Recommended use of CLUSTERING KEYS, MATERIALIZED VIEWS, and proper data types to enhance query performance and data storage.

Phase 4: Governance and Automation for Scale

  • Infrastructure as Code (IaC): Started managing Snowflake infrastructure using Terraform, enabling version-controlled, repeatable provisioning of warehouses, users, and roles.
  • Automated Auditing: Created reporting dashboards using ACCESS_HISTORY and QUERY_HISTORY to monitor data usage, user activity, and policy adherence.
  • Established regular review cadences to reassess warehouse performance, access policies, and schema alignment

Outcome:

By optimizing Snowflake’s core setup, the client built a scalable, secure, and cost-efficient data platform that powers smarter decisions, stronger player engagement, and sustained business growth.

  • 22% Cost Reduction due to Smart sizing, auto-suspend, and monitors cut Snowflake credits.
  • Faster Insights as Dashboards and queries performed better across teams.
  • Improved Security & Compliance with fine-grained RBAC and audit logs
  • Unified Data Architecture with Medallion design, it reduced duplication and improved trust.
  • Scalable Setup which is now ready to onboard new platforms and data sources effortlessly.

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