Back to Success Stories
01
The Challenge
The organization had a growing Looker and BigQuery analytics platform that was becoming increasingly critical for business decisions. However, as usage scaled, several pain points emerged:
- Slow dashboard performance: Critical business dashboards were taking too long to load, frustrating users and reducing adoption across the organization.
- 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.
02
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.
LookML
Derived Tables
PDTs
Datagroups
Cache Policies
03
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.
BigQuery
Partitioning
Clustering
FinOps
Cost Optimization
04
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.
05
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.
Python
Looker API
Automation
SDK
06
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.
07
The Results
The comprehensive optimization delivered measurable impact across all dimensions:
↓ 40%+
Dashboard Load Times
↑ 3x
Business Self-Service
↓ 80%
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.
Is Your Looker Platform Underperforming?
Let's discuss how RavencoreX can optimize your Looker & BigQuery environment for speed, cost-efficiency, and scale.
Get a Free Performance Audit