Enterprise-Style Football Analytics Platform built with Databricks Lakehouse, Delta Live Tables, Unity Catalog, CI/CD, and Power BI Semantic Modeling.
The Football Analytics Lakehouse is a modern enterprise-style analytical platform built on Databricks using Lakehouse architecture principles, Medallion data modeling, declarative pipelines, metadata-driven engineering, and centralized governance.
The platform simulates real-world analytical engineering patterns commonly used in scalable enterprise data platforms while focusing on football analytics, tactical reporting, observability monitoring, semantic analytical serving, and future advanced analytics evolution.
The project uses StatsBomb Open Data as its primary source system and was intentionally designed to emphasize enterprise engineering best practices instead of isolated notebook experimentation.
The platform was designed to achieve the following objectives:
- simulate a real-world enterprise Lakehouse platform
- implement scalable Medallion Architecture patterns
- demonstrate modern Databricks engineering practices
- build reusable analytical football datasets
- support tactical and scouting analytics
- implement governance and metadata management
- implement observability and monitoring patterns
- create Power BI semantic-ready datasets
- demonstrate CI/CD deployment automation
- prepare the platform for future advanced analytical evolution
The platform follows a modern Medallion Architecture approach:
StatsBomb Open Data
│
▼
Unity Catalog Volumes
│
▼
Bronze Streaming Tables
│
▼
Silver Standardized Streaming Tables
│
▼
Gold Analytical Serving Layer
│
├── Power BI Dashboards
├── Tactical Analytics
├── Observability Layer
└── Future Advanced Analytics
| Component | Technology |
|---|---|
| Lakehouse Platform | Databricks |
| Storage Layer | Delta Lake |
| Governance Layer | Unity Catalog |
| Pipeline Framework | Delta Live Tables |
| Orchestration | Lakeflow Jobs |
| CI/CD | GitHub Actions |
| Infrastructure Deployment | Databricks Asset Bundles |
| Source Control | GitHub |
| Development Environment | VS Code |
| BI Layer | Power BI |
| Primary Language | SQL |
| Source Dataset | StatsBomb Open Data |
The Bronze layer preserves raw source fidelity and ingestion lineage.
- raw ingestion
- source preservation
- operational metadata
- ingestion lineage
- raw_competitions
- raw_matches
- raw_lineups
- raw_events
The Silver layer standardizes and validates football event structures.
- event normalization
- semantic organization
- data quality enforcement
- specialized analytical structures
- events
- shots
- passes
- carries
- dribbles
- pressures
- duels
- fouls
- goalkeeper_actions
- substitutions
- event_related_events
The Gold layer delivers analytical, tactical, semantic, and observability datasets optimized for Power BI and advanced analytical consumption.
- KPI generation
- tactical analysis
- semantic modeling
- observability monitoring
- advanced analytical preparation
- match_summary
- match_momentum
- match_timeline
- team_match_stats
- team_season_stats
- team_offensive_metrics
- team_defensive_metrics
- player_match_stats
- player_season_stats
- player_offensive_metrics
- player_defensive_metrics
- shot_events
- pressure_events
- shot_zones
- pressure_zones
- passing_network
- possession_sequences
- dim_match
- dim_team
- dim_player
- dim_match_time_window
- pipeline_table_metrics
- pipeline_freshness_metrics
- pipeline_quality_metrics
- pipeline_execution_metrics
The platform uses:
- Delta Live Tables (DLT)
- Streaming Tables
- Materialized Views
- Declarative SQL Pipelines
- Databricks Asset Bundles (DABs)
- Unity Catalog Governance
- Serverless Compute
The platform implements enterprise-grade governance patterns using Unity Catalog.
- semantic table comments
- TBLPROPERTIES metadata
- Unity Catalog TAGS
- metadata-driven discovery
- governance-as-code
- lineage visibility
- centralized governance
The platform heavily adopts metadata-driven engineering principles.
COMMENT "Gold analytical model containing player-level offensive KPIs."TBLPROPERTIES (
'data_domain' = 'football_analytics',
'data_layer' = 'gold',
'owner_team' = 'analytics_engineering'
)SET TAGS (
'layer' = 'gold',
'consumption_type' = 'power_bi'
)The Silver layer implements Delta Live Tables Expectations for data quality enforcement.
CONSTRAINT valid_event_id EXPECT (
event_id IS NOT NULL
)- schema reliability
- semantic consistency
- downstream analytical integrity
- tactical analytical reliability
The platform includes lightweight observability models directly inside the Lakehouse.
- pipeline_table_metrics
- pipeline_freshness_metrics
- pipeline_quality_metrics
- pipeline_execution_metrics
- row count monitoring
- freshness validation
- quality validation
- execution health visibility
The platform uses GitHub Actions and Databricks Asset Bundles for deployment automation.
VS Code
↓
Git Commit
↓
Git Push
↓
GitHub Actions
↓
DEV Deployment
↓
Approval Gate
↓
PROD Deployment
- automated validation
- DEV deployment automation
- PROD approval gate
- reproducible deployments
- governance-as-code
- environment isolation
The deployment architecture includes:
- controlled production promotion
- manual approval workflows
- isolated environments
- deployment validation
- reproducible infrastructure deployment
The Gold layer was intentionally designed for scalable semantic modeling inside Power BI.
- reusable dimensions
- star-schema orientation
- semantic consistency
- analytical scalability
- tactical slicing capabilities
- dim_match
- dim_team
- dim_player
- dim_match_time_window
- executive dashboards
- tactical analysis
- scouting analysis
- spatial analytics
- observability analytics
Main dashboard capabilities:
- match momentum tracking
- possession analysis
- team comparison
- tactical metrics
- player contribution analytics
- match event KPIs
Custom tactical visualization using event coordinates and time-window segmentation for advanced spatial analysis.
Pressure zone analytics enable defensive intensity analysis and tactical positioning insights.
The platform includes advanced football analytical models including:
- passing networks
- possession sequences
- pressure zones
- shot zones
- momentum analysis
- offensive intensity indicators
- defensive intensity indicators
The Gold layer includes specialized analytical models such as:
| Model | Description |
|---|---|
| match_summary | Match-level KPIs |
| match_momentum | Time-window momentum analysis |
| pressure_zones | Defensive pressure spatial analysis |
| passing_network | Team passing interaction analysis |
| player_match_stats | Player-level match performance |
| player_season_stats | Season aggregated player metrics |
| shot_events | Shot analytics |
| team_match_stats | Team performance KPIs |
football-analytics-lakehouse/
│
├── .github/
│ └── workflows/
│
├── docs/
│ ├── architecture.md
│ ├── architecture-diagrams.md
│ ├── governance.md
│ ├── powerbi-semantic-model.md
│ └── images/
│
├── resources/
│ ├── bronze_pipeline.yml
│ ├── silver_pipeline.yml
│ ├── gold_pipeline.yml
│ └── orchestrator.job.yml
│
├── src/
│ ├── bronze/
│ ├── silver/
│ ├── gold/
│ └── ingestion/
│
├── databricks.yml
│
└── README.md
This project demonstrates practical implementation of:
- Medallion Architecture
- Streaming Data Engineering
- Declarative Pipelines
- Data Governance
- CI/CD for Data Platforms
- Semantic Modeling
- Tactical Sports Analytics
- Metadata Management
- Data Lineage
- Enterprise Data Architecture
- Analytics Engineering
- Lakehouse Design Patterns
Detailed documentation is available in:
| Document | Description |
|---|---|
| architecture.md | Enterprise platform architecture |
| architecture-diagrams.md | Mermaid architecture diagrams |
| governance.md | Governance and metadata strategy |
| powerbi-semantic-model.md | Power BI semantic modeling strategy |
Potential future enhancements include:
- Machine Learning feature engineering
- Expected Goals (xG) modeling
- Real-time streaming ingestion
- Lakehouse Monitoring integration
- Data Quality dashboards
- Automated data observability
- Feature Store integration
- Advanced tactical clustering models
This project was intentionally designed to simulate real-world modern data platform engineering practices including:
- enterprise governance
- modular architecture
- reusable analytical modeling
- CI/CD automation
- observability-first engineering
- semantic data modeling
- scalable analytical serving
This project demonstrates how modern enterprise data engineering practices can be applied to sports analytics using the Databricks Lakehouse Platform.
The platform combines:
- scalable data architecture
- governance
- streaming pipelines
- CI/CD
- semantic modeling
- analytical storytelling
into a fully integrated analytics solution.
Renan Vitor Nyko
LinkedIn:
https://www.linkedin.com/in/renannyko/
GitHub:
https://github.com/renannyko