Telecom Digital Twin

Overview

Telecom Digital Twin is a deterministic, schema-first telecom data generator that produces a multi-table LTE dataset (users, cells, sessions, events) with causal ordering and reproducible outputs. Designed as an MLOps testbed, it generates realistic telecom data without private information.

The system uses cascade-based seeding where seeds are derived from global seed and step ID, ensuring bit-exact reproducibility. It enforces strict schema validation with Apache Parquet for columnar compression and embedded schema metadata. All outputs maintain referential integrity with foreign key validation.

Built for ML-ready data generation, the pipeline produces clean, validated datasets with proper correlations across infrastructure, behavior, performance, and events. It supports fast development mode for quick iteration and full-scale generation for production use.

Key Metrics & Results

Problem Statement

Production telecom data is proprietary and sensitive, making it unavailable for ML development and testing. Off-the-shelf synthetic data tools lack domain expertise and produce unrealistic correlations. ML pipelines require deterministic, reproducible data for debugging and validation.

Business Context

Data scientists need realistic telecom datasets for model development without accessing production data. ML pipelines require bit-exact reproducibility to debug failures. Data quality issues (orphan records, type mismatches) break downstream JOINs and model training.

Technical Challenges

• Generating realistic telecom data with proper physics-based correlations
• Ensuring bit-exact reproducibility across pipeline runs
• Maintaining referential integrity across multiple tables
• Optimizing storage format for large-scale data generation
• Validating schema consistency and data quality

Solution Architecture

A seven-step pipeline: (1) Schema design with canonical definitions, (2) Cells generation with infrastructure metadata, (3) Users generation with demographics, (4) Behavior generation (user + network), (5) Sessions generation with KPIs and QoE, (6) Events generation with causal signals, (7) Validation with referential integrity checks.

System Components

Deterministic Seeding System

Cascade-based seeding where Seed(Step_N) = F(Seed(Global), Step_ID). Ensures bit-exact reproducibility across runs. All generators use fixed seeds and explicit config values.

Parquet Storage Layer

Enforces Apache Parquet for all outputs. Provides columnar compression and embedded schema metadata ensuring type consistency (float32 remains float32).

Schema-First Generation

Strict schema enforcement with validation at generation time. Foreign key validation in Step 07 acts as unit test for data integrity, raising exceptions if referential integrity violated.

Causal Ordering System

Generates data with proper causal relationships: infrastructure → behavior → performance → events. Network-level events omit user/session IDs; user events include IDs.

Technology Stack Rationale

Parquet chosen over CSV for schema enforcement and compression. Deterministic seeding enables reproducible ML pipelines. Schema validation prevents downstream JOIN failures. Causal ordering ensures realistic data relationships. SDV provides domain-informed generation.

Implementation Highlights

Key Features

• Bit-Exact Reproducibility: Cascade-based seeding ensures identical outputs across runs with same config
• Referential Integrity Validation: Step 07 validates foreign keys, raising exceptions if orphan records detected
• Parquet Columnar Storage: Columnar compression with embedded schema metadata
• Fast Development Mode: Quick sampling (5K users, 7 days) for rapid iteration
• Physics-Based Correlations: Realistic network KPIs with proper correlations (SINR, congestion, QoE)

Challenges & Solutions

Results & Impact

Generated production-scale dataset: 50K users, 2K cells, 5.6M sessions, 104K events. Achieved reproducible outputs with deterministic seeding. Validated referential integrity with foreign key checks. Parquet compression provides efficient storage with embedded schema metadata.

Lessons Learned

What Worked Well

• Deterministic seeding enabled perfect reproducibility for ML debugging
• Parquet storage provided significant I/O optimization with schema enforcement
• Schema-first approach prevented downstream data quality issues
• Causal ordering created realistic data relationships
• Validation step caught data issues before promotion

What I'd Do Differently

• Would add incremental generation for large-scale datasets
• Should implement parallel processing for behavior generation step
• Could add more sophisticated network physics modeling
• Would add data quality metrics dashboard

Future Enhancements

• Add incremental generation for large-scale datasets
• Implement parallel processing for behavior generation
• Add more sophisticated network physics (5G NR, mmWave)
• Build data quality metrics dashboard

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