Data silos everywhere
Critical business data scattered across multiple systems — CRMs, ERPs, marketing platforms, e-commerce backends — with no unified view.
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Data Architecture
Building complete BI platforms from the ground up
How RavenCoreX implements end-to-end Business Intelligence architectures — from data ingestion and transformation to high-performance analytics on Google Cloud and Looker.
Enterprise Consulting Clients (Tech & E-commerce)
100%
Data source unification
↓ 90%
Report generation time
↑ 5x
User adoption
Zero
Business disruption
01 — The challenge
Fragmented data that blocks decisions
Enterprise organizations often face fragmented data landscapes that prevent them from making data-driven decisions. Common scenarios we encounter:
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Outdated BI tools like SSRS or Power BI with complex, unmaintainable reports that don't scale and are disconnected from source data.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
A modern, cloud-native data stack
We design and implement complete data architectures following modern best practices and cloud-native patterns:
Data Sources
→
Ingestion (Airbyte)
→
Transformation (DBT)
→
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.
03 — Looker implementation
A semantic layer as the single source of truth
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.
04 — Migration & transformation
Off legacy systems, with zero disruption
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
Enterprise-grade governance from the start
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
Built to run, and to hand off
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.
07 — The results
Transformational impact across the organization
End-to-end implementations deliver measurable impact across the organization:
100%
Data source unification
↓ 90%
Report generation time
↑ 5x
User adoption
Zero
Business disruption
- 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.
Your data, next
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