Slow dashboard performance
Critical business dashboards were taking too long to load, frustrating users and reducing adoption across the organization.
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Optimization
Deep tuning of a high-volume Looker & BigQuery analytics platform
How RavenCoreX optimized, scaled and governed an enterprise analytics platform serving intensive daily business usage — across performance, FinOps, governance and automation.
SaaS & Automotive Marketing leader (U.S.)
40%+
faster dashboard load times
30%+
lower BigQuery costs
3x
more business self-service
80%
fewer manual reporting tasks
The challenge
A critical platform that was getting slower and more expensive as it scaled
The organization had a growing Looker and BigQuery analytics platform that was becoming increasingly critical for business decisions. As usage scaled, several pain points emerged.
Escalating BigQuery costs
Unoptimized queries and lack of proper data architecture were driving up cloud costs significantly month over month.
Governance gaps
Fragmented folder structures, inconsistent permissions, and duplicated logic across LookML models made maintenance increasingly difficult.
Limited self-service
Business users couldn't effectively explore data on their own, creating bottlenecks and constant dependency on the data team.
Manual operational overhead
Repetitive reporting tasks were consuming valuable engineering time that could be better spent on strategic work.
The goal was clear: transform the analytics platform into a fast, cost-efficient, governed, and self-service-ready foundation that could scale with the business.
The approach · Looker
Looker architecture & performance
We executed a comprehensive optimization of the Looker environment, applying Google's best practices and deep platform expertise.
LookML model refactoring
Complete redesign of models following best practices — proper use of extends, constants, and modular structures for maintainability.
Explore & View optimization
Streamlined Explores and Views to reduce query complexity, optimized joins to minimize unnecessary data scans.
PDT strategy implementation
Implemented Persistent Derived Tables strategically for frequently-used aggregations, dramatically reducing query times.
Datagroups & caching policies
Configured intelligent caching strategies aligned with data freshness requirements, balancing performance with data currency.
The approach · BigQuery
BigQuery optimization & FinOps
We implemented a complete FinOps approach to control and reduce cloud data costs while improving query performance.
Query refactoring
Analyzed and refactored complex SQL queries to reduce bytes scanned, eliminate redundant operations, and leverage BigQuery's optimization capabilities.
Partitioning & clustering
Implemented appropriate partitioning and clustering strategies aligned with actual data consumption patterns and query filters.
Cost analysis & monitoring
Established visibility into query costs by dashboard, user, and schedule — enabling data-driven decisions on optimization priorities.
Resource efficiency strategies
Defined policies for efficient resource usage in production environments, including slot management and query prioritization.
The approach · Governance
Governance & semantic modeling
We established enterprise-grade governance to ensure the platform remained maintainable and secure as it scaled.
Semantic model redesign
Restructured analytical models to improve maintainability, enable business self-service, and eliminate duplicated logic across the platform.
User attributes & access policies
Implemented sophisticated user attributes, hierarchies, and data access policies to ensure users see only what they should.
Content governance
Reorganized folder structures, established naming conventions, and defined clear ownership and permissions for all Looker content.
Security & compliance
Implemented strict access controls ensuring compliance with internal security and privacy policies, with clear separation between environments.
The approach · Automation
Automation & Looker API
We developed automation solutions to eliminate manual overhead and enable scalable operations.
Python + Looker API scripts
Built custom automation using Looker API to automate report generation, scheduled deliveries, and operational tasks.
Reduced manual dependencies
Eliminated repetitive manual tasks that were consuming engineering time, freeing the team for strategic work.
System integrations
Supported integrations between Looker and other internal systems, enabling automated data flows and notifications.
The approach · Adoption
User experience & adoption
Beyond technical optimization, we focused on ensuring real business adoption of the platform.
Dashboard UX improvements
Enhanced look & feel with clear navigation, visual hierarchy, and consistent metric presentation across all dashboards.
Dynamic filtering & personalization
Implemented smart filters and custom logic adapted to different user profiles and roles.
Self-service enablement
Designed the semantic layer to empower business users to explore data independently without breaking things.
Training & documentation
Provided guidance to internal teams on best practices and platform capabilities.
The results
Measurable impact across every dimension
The comprehensive optimization delivered measurable impact across performance, cost, autonomy and operations.
40%+
faster dashboard load times
30%+
lower BigQuery costs
3x
more business self-service
80%
fewer manual reporting tasks
- Faster, more reliable dashboards: Critical business dashboards now load significantly faster, driving increased user adoption and trust in the platform.
- Reduced operational costs: BigQuery costs decreased substantially through query optimization, proper partitioning, and intelligent caching strategies.
- Greater business autonomy: Business users can now self-serve for analytics and reporting needs, reducing dependency on the data team.
- Scalable, governed platform: The analytics infrastructure is now properly governed, maintainable, and ready to scale with business growth.
- Automated operations: Key processes that were previously manual are now automated, freeing engineering time for higher-value work.
Your platform, next
Is your Looker platform underperforming?
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