MARK FAHAD

Unified customer data platform with Neo4j graph analytics for entity resolution, built on Snowflake with dbt ELT pipelines and Unity Catalog governance.

Customer 360 Platform with Graph Analytics

  • Category : Graph Analytics / Data Platform
  • Technologies : Snowflake, Neo4j, dbt, Unity Catalog
  • GitHub : View Repository
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Project Overview

Designed a comprehensive Snowflake data platform integrated with Neo4j for advanced entity resolution across multiple customer touchpoints. Built dbt ELT pipelines orchestrating data from 20+ sources with Unity Catalog governance for metadata management and lineage tracking. Enabled personalized marketing campaigns increasing customer lifetime value by 30%.

Entity Resolution with Neo4j

Implemented sophisticated entity resolution using Neo4j graph database to create unified customer profiles across multiple channels and identifiers. The graph-based approach links customers through email addresses, phone numbers, devices, and IP addresses, providing a complete 360-degree view of customer interactions and relationships.

Snowflake Data Warehouse

Designed Snowflake data warehouse using Kimball dimensional modeling optimized for analytical queries. Implemented clustering keys and materialized views for performance optimization, reducing query times by 60% and warehouse costs by 30%. The warehouse serves as the central repository for all customer data and analytics.

dbt ELT Pipelines

Built modular dbt models for data transformation with comprehensive testing and documentation. Orchestrated with Airflow for scheduled execution managing dependencies across 20+ data sources. Implemented CI/CD workflows automating dbt model testing and deployment, reducing release cycles from weeks to days.

  • 01Graph Analytics

    Neo4j entity resolution creating unified customer profiles across multiple touchpoints.

  • 03Data Governance

    Unity Catalog ensuring metadata management and lineage tracking across the platform.

  • 02ELT Pipelines

    dbt and Airflow orchestrating data from 20+ sources into curated data models.

  • 04Customer Segmentation

    ML-powered segmentation feeding personalized marketing campaigns.

Results & Impact

The Customer 360 platform enabled personalized marketing campaigns increasing customer lifetime value by 30% and conversion rates by 25%. Successfully unified data across multiple channels providing marketing teams with actionable insights. Reduced data warehouse costs by 30% through optimization while improving query performance by 60%. The platform now serves as the single source of truth for all customer analytics and reporting.

frequently asked questions

  • What is Customer 360 and why is it important?
    Customer 360 unifies data from 20+ sources including web, mobile, CRM, and transactions to create a complete customer view. Enables personalized marketing, increasing customer lifetime value by 30% and conversion rates by 25%.
  • How does the platform handle data integration?
    Uses Kafka and Debezium for real-time CDC from source databases, dbt for data transformation, and Airflow for workflow orchestration. All data flows into a Delta Lake on Databricks for unified storage and processing.
  • How does entity resolution work?
    Neo4j graph database links customers across email, phone, device, and IP addresses. Graph traversal algorithms identify relationships and create unified customer profiles even across multiple identifiers and touchpoints.
  • What about data governance and security?
    Databricks Unity Catalog ensures comprehensive data governance with fine-grained access control, metadata management, and full data lineage tracking. PII data is encrypted and access is logged for audit compliance.
  • What were the platform results?
    30% increase in customer lifetime value, 25% higher conversion rates, 60% faster query performance, 30% reduced warehouse costs, and unified analytics across all customer touchpoints as single source of truth.

Contact For Opportunities

project budget