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EV Charging Data Warehouse & BI System Architecture

Executive Summary

This project implements a comprehensive data warehouse and business intelligence solution for Electric Vehicle (EV) charging infrastructure analytics. Built using Microsoft SQL Server stack, it demonstrates end-to-end data engineering capabilities from multi-source data integration through advanced analytics and interactive dashboards.

Business Context

Industry Challenge

The EV charging industry generates massive volumes of usage data that must be analyzed for:

  • Operational Efficiency: Station utilization and maintenance planning
  • Revenue Optimization: Pricing strategies and peak demand management
  • Customer Insights: User behavior patterns and service improvements
  • Infrastructure Planning: Network expansion and capacity management

Solution Value

  • Real-time Analytics: Fast query performance on large datasets
  • Multi-dimensional Analysis: Weather, holiday, and geographic impact assessment
  • Scalable Architecture: Design supporting future growth
  • Actionable Insights: Data-driven decision making capabilities

System Architecture Overview

DW & BI Solution Architecture

Architecture Philosophy

Following Kimball's Dimensional Modeling approach with modern enterprise patterns:

Sources Integration -> Staging Layer -> ETL Processing -> Data Warehouse -> OLAP Cube -> BI Analytics

Data Flow Architecture

ETL Pipeline Architecture

1. Data Sources Layer

Multi-format heterogeneous data sources simulating real-world enterprise environment:

Source Type Format Volume Purpose Integration Method
Charging Sessions CSV 100K+ records Core transaction data SSIS Flat File Source
User Information TXT 15K+ records Customer master data SSIS Flat File Source
Weather Data Excel 365+ days Environmental context SSIS Excel Source
Holiday Calendar SQL Server 50+ records Seasonal analysis SSIS OLE DB Source

2. Staging Layer

Enterprise data staging with quality assurance:

  • Raw Data Preservation: Original format retention for audit trails
  • Data Profiling: Automated quality checks and anomaly detection
  • Cleaning & Standardization: Format normalization and validation
  • Reprocessing Capability: Support for data reloads and corrections

3. ETL Processing Layer (SSIS)

Microsoft SQL Server Integration Services enterprise ETL:

  • Package 1: Source to Staging data ingestion
  • Package 2: Staging data profiling and quality validation
  • Package 3: Staging to Data Warehouse transformation
  • Package 4: Accumulating snapshot updates for SCD Type 2

Key Transformations:

  • Data type conversions and validations
  • Business rule implementations
  • Slowly Changing Dimensions (SCD Type 2)
  • Surrogate key generation
  • Data enrichment and calculations

4. Data Warehouse Layer

Enterprise-grade star schema data warehouse:

Star Schema Design

Conceptual Data Model

Fact Table: FactChargingSessions

  • Grain: One row per charging segment per port usage event
  • Measures: Energy_kWh, Fee, GHG_Savings, Gasoline_Savings
  • Immutable Design: Append-only for historical accuracy

Dimension Tables:

  • DimDate: Time hierarchy (Year-Quarter-Month-Day)
  • DimStation: Geographic hierarchy (Country-State-City-Station)
  • DimPort: Equipment specifications and capabilities
  • DimUser: Customer master data (SCD Type 2 for location history)
  • DimWeather: Environmental conditions and impacts
  • DimHoliday: Seasonal and event-based analysis

5. OLAP Layer (SSAS)

SQL Server Analysis Services multidimensional cube:

Cube Design Features:

  • Pre-aggregated Measures: Energy consumption, revenue, environmental impact
  • Hierarchies: Time, geography, and equipment dimensions
  • Calculated Members: KPIs, ratios, and business metrics
  • Perspectives: Role-based data views
  • Partitions: Performance optimization for large datasets

Performance Optimizations:

  • Aggregation design for query acceleration
  • Processing strategies for minimal downtime
  • Storage mode optimization (MOLAP/HOLAP)

6. Business Intelligence Layer

Multi-platform analytics delivery:

Power BI Dashboards

  • DirectQuery Connection: Live SSAS cube connectivity
  • Interactive Visualizations: Drill-through, slicers, and hierarchies
  • KPI Monitoring: Real-time performance metrics
  • Mobile Responsive: Cross-platform accessibility

Excel OLAP Analysis

  • PivotTable Integration: Native Excel cube connectivity
  • OLAP Operations: Roll-up, drill-down, slice, dice, pivot
  • Ad-hoc Analysis: User-driven exploration capabilities

Technology Stack

Core Technologies

Component Technology Version Purpose
Database Engine SQL Server 2019+ Data storage and processing
ETL SSIS 2019+ Data integration and transformation
OLAP SSAS 2019+ Multidimensional analytics
BI Power BI Desktop Latest Interactive dashboards
Analysis Excel 2016+ OLAP pivot analysis

Supporting Technologies

  • SQL Server Management Studio (SSMS): Database administration
  • SQL Server Data Tools (SSDT): ETL and OLAP development
  • Power BI Service: Cloud sharing and collaboration
  • Windows Server: Production deployment platform

Technical Implementation

Data Integration Approach

  • Multi-source ingestion: CSV, TXT, Excel, SQL Server sources
  • Data quality validation: Automated profiling and checks
  • Dimensional modeling: Star schema with conformed dimensions
  • Business logic implementation: Calculations and transformations

Project Outcomes

Technical Achievements

  • End-to-end data pipeline: From source to analytics
  • Multi-dimensional analysis: Time, geographic, and environmental factors
  • Interactive dashboards: Power BI with SSAS connectivity
  • Performance optimization: Query acceleration and indexing

Learning Outcomes

  • Data engineering skills: ETL design and implementation
  • Business intelligence: Dashboard development and KPI design
  • Database architecture: Star schema and dimensional modeling
  • Analytics implementation: OLAP cube and reporting solutions

This architecture demonstrates practical implementation of data warehouse and BI concepts using Microsoft SQL Server stack.