Back to Success Stories
01
The Challenge
Enterprise organizations often face fragmented data landscapes that prevent them from making data-driven decisions. Common scenarios we encounter:
- Data silos everywhere: Critical business data scattered across multiple systems — CRMs, ERPs, marketing platforms, e-commerce backends — with no unified view.
- Legacy reporting systems: Outdated BI tools like SSRS or Power BI with complex, unmaintainable reports that don't scale and are disconnected from source data.
- No single source of truth: Different teams using different definitions for the same metrics, leading to conflicting reports and eroded trust in data.
- Manual, error-prone processes: Data extraction and reporting done through spreadsheets and manual exports, consuming valuable time and introducing errors.
- Scalability limitations: Existing infrastructure unable to handle growing data volumes and user demands, causing performance degradation.
The goal: Build a modern, scalable, governed analytics platform that unifies all data sources and empowers the entire organization with self-service BI capabilities.
02
The Architecture
We design and implement complete data architectures following modern best practices and cloud-native patterns:
Data Sources
→
Ingestion (Airbyte)
→
Transformation (DBT)
→
Data Warehouse (BigQuery)
→
Semantic Layer (Looker)
- Data ingestion layer: Automated data pipelines pulling from multiple sources — HubSpot, Shopify, cloud databases (Azure, GCP), APIs, and legacy systems.
- Transformation layer: DBT Cloud for scalable, version-controlled data transformations with proper testing, documentation, and lineage tracking.
- Data warehouse: BigQuery as the central repository, with proper partitioning, clustering, and cost optimization from day one.
- Orchestration: Airflow or Cloud Composer for reliable scheduling, monitoring, and dependency management across the entire pipeline.
BigQuery
DBT Cloud
Airbyte
Airflow
Google Cloud
03
Looker Implementation
We build Looker environments from scratch following Google's best practices, creating a true semantic layer that serves as the single source of truth:
- LookML model design: Clean, modular model architecture with proper use of extends, constants, and reusable components for long-term maintainability.
- High-performance Explores: Optimized Explores prioritizing usability, performance, and data accuracy — designed for both analysts and business users.
- Advanced calculations: Period-over-period comparisons, complex KPIs, dynamic metrics — all encapsulated in the semantic layer for consistent reporting.
- PDTs and caching strategy: Strategic implementation of Persistent Derived Tables and Datagroups to balance performance with data freshness requirements.
LookML
Explores
PDTs
Datagroups
Custom Visualizations
04
Migration & Transformation
Many implementations involve migrating from legacy systems while preserving business logic and ensuring zero disruption:
- Legacy system migration: Structured approach to migrating from SSRS, Power BI, Tableau, and other platforms to the modern BigQuery + Looker stack.
- Business logic preservation: Careful reimplementation of complex business rules in SQL and LookML, with thorough validation against original reports.
- Functional validation: Side-by-side comparison with business users to ensure metrics match expectations before decommissioning legacy systems.
- Zero-downtime transition: Parallel operation periods allowing users to validate the new platform while maintaining access to familiar tools.
05
Governance & Security
We establish enterprise-grade governance from the start, ensuring the platform remains secure, maintainable, and scalable:
- Access control architecture: User attributes and row-level security enabling the same dashboards to serve multiple audiences with appropriate data access.
- Content organization: Logical folder structures, naming conventions, and clear ownership definitions for all Looker content.
- Code quality standards: Reusable patterns, separation of concerns, and documentation practices that reduce technical debt over time.
- Security compliance: Implementation aligned with internal security policies, data privacy requirements, and industry regulations.
06
Automation & Operations
We automate operational tasks and establish sustainable practices for long-term platform health:
- Python automation: Custom scripts for operational tasks, data validation, and integration with the Looker SDK for advanced workflows.
- CI/CD pipelines: GitHub-based version control with automated testing and deployment for both DBT and LookML changes.
- Monitoring & alerting: Proactive monitoring of pipeline health, data freshness, and query performance with appropriate alerting.
- Documentation & handoff: Comprehensive documentation enabling internal teams to maintain and extend the platform independently.
Python
Looker SDK
GitHub
CI/CD
07
The Results
End-to-end implementations deliver transformational impact across the organization:
100%
Data Source Unification
↓ 90%
Report Generation Time
- Single source of truth: All business data unified in one platform with consistent metrics and definitions across the organization.
- Self-service analytics: Business users empowered to explore data and create their own reports without dependency on technical teams.
- Faster time-to-insight: Reports that used to take days now available in seconds, with automated refresh and delivery.
- Scalable foundation: Architecture designed to grow with the business, handling increasing data volumes and user demands.
- Reduced technical debt: Clean, maintainable codebase that enables rather than hinders future enhancements.
Ready to Build Your Modern Analytics Platform?
Let's discuss how RavencoreX can design and implement a complete BI architecture tailored to your business needs.
Schedule a Discovery Call