Telecom Provider Builds Modern Data Lake Architecture
The Challenge
A leading telecommunications provider serving 28 million subscribers across Asia Pacific was drowning in data silos that prevented advanced analytics and AI initiatives. The company generated 3 petabytes of data annually from network infrastructure, customer interactions, billing systems, and IoT devices, but this valuable data was trapped in 200+ disconnected systems with no unified architecture. Data scientists spent 70% of their time on data access and preparation rather than building models, severely limiting the organization's ability to compete with digital-native competitors. The fragmented data landscape made it impossible to create a 360-degree customer view, limiting personalization and driving customer churn. Network optimization initiatives couldn't access performance data in real-time, resulting in reactive rather than proactive infrastructure management. The company's ambitious AI roadmap - including predictive churn modeling, network anomaly detection, and personalized service recommendations - was stalled due to lack of unified data infrastructure. The CTO received board mandate to build a modern data lake architecture consolidating all enterprise data, enabling self-service analytics for 500+ data consumers, and establishing the foundation for AI-powered services. The project had to be completed within 12 months to support the company's digital transformation timeline.
The Strategy
- 1 Design modern data lake architecture on Azure with bronze/silver/gold data layers
- 2 Implement scalable ingestion framework handling 200+ source systems and real-time streams
- 3 Deploy Databricks lakehouse platform enabling both data engineering and data science workloads
- 4 Establish data governance framework with cataloging, lineage, and access controls
🏗️ Data Lake Architecture Design
The Problem We Found
No unified data architecture existed. Each department built their own data solutions, creating 200+ disconnected systems. Data was stored in incompatible formats across on-premises data centers, legacy mainframes, and multiple cloud providers. No data standards or governance framework existed.
Our Approach
- Designed medallion architecture (bronze/silver/gold layers) on Azure Data Lake Gen2
- Bronze layer: Raw data ingestion from all sources in native formats with immutable storage
- Silver layer: Cleansed, validated, and conformed data using Delta Lake for ACID transactions
- Gold layer: Business-ready aggregated datasets optimized for analytics and ML
- Implemented lifecycle policies automatically moving data between hot/cool/archive tiers based on usage patterns
The Result
Successfully architected scalable data lake handling 3PB of enterprise data with room for 10x growth. Medallion architecture provides clear data quality progression from raw to business-ready. Lifecycle management reduced storage costs by 60% through intelligent tiering. All 200+ source systems now feeding into unified platform.
Metrics
⚡ Scalable Data Ingestion & Processing
The Problem We Found
No standardized ingestion framework existed. Each data pipeline was custom-built, creating maintenance nightmare. Real-time streaming data from network infrastructure had no processing capability. Batch ETL jobs took 18-24 hours, making data stale before it was usable.
Our Approach
- Built Azure Data Factory orchestration framework with reusable ingestion templates
- Implemented event-driven ingestion using Azure Event Hubs for real-time network telemetry
- Created Databricks Auto Loader for incremental processing of new files in data lake
- Established PySpark-based transformation pipelines for large-scale data processing
- Deployed Apache Airflow for complex workflow orchestration and dependency management
- Implemented data quality framework with automated validation rules and anomaly detection
The Result
Unified ingestion framework now handles 200+ source systems with 90% code reuse through templates. Real-time streaming processes 5TB daily of network telemetry with sub-second latency. Batch processing improved from 18-24 hours to 3-4 hours through Databricks optimization. Data quality checks catch 99% of issues before reaching silver layer.
Metrics
🤖 Databricks Lakehouse & ML Platform
The Problem We Found
Data scientists had no access to unified data platform and spent 70% of time on data access/preparation. No MLOps framework existed for model deployment. Separate infrastructure for analytics and ML created duplication and inconsistency. Notebooks couldn't be version-controlled or collaboratively developed.
Our Approach
- Deployed Databricks unified lakehouse platform combining data warehouse and ML capabilities
- Created self-service workspace for 500+ data consumers with role-based access controls
- Implemented Delta Lake for ACID transactions, time travel, and schema evolution
- Built feature store centralizing ML features for reuse across models and consistency
- Established MLflow for experiment tracking, model registry, and automated deployment
- Deployed Unity Catalog for centralized governance, data lineage, and access auditing
- Created pre-configured cluster policies optimizing costs while ensuring performance
The Result
Data scientists now spend 80% of time on modeling instead of data preparation. 500+ users access unified lakehouse through self-service interface. Feature store reduced feature engineering time by 60% through reuse. MLflow automated model deployment, reducing time-to-production from months to days. Unity Catalog provides complete data lineage and automated compliance reporting.
Metrics
Impact & Results
The modern data lake architecture transformed the telecommunications provider into a data-driven organization capable of competing with digital-native competitors. Consolidating 3PB of data from 200+ silos into a unified platform unlocked previously impossible analytics and AI initiatives. Customer churn prediction models built on the lakehouse reduced churn by 18%, saving $67M annually in customer acquisition costs. Network optimization algorithms running on real-time telemetry improved infrastructure utilization by 35%, deferring $180M in planned capital expenditures. Data scientists now deploy ML models in 5-10 days instead of 3-6 months, accelerating innovation velocity. The 500+ data consumers accessing the platform through self-service interfaces created a culture of data-driven decision making across the organization. Storage costs decreased 60% despite handling 10x more data through intelligent lifecycle management. The unified lakehouse established the foundation for the company's digital transformation, enabling personalized customer experiences, predictive network management, and AI-powered service innovation.
"Zatsys architected a data platform that became the foundation of our digital transformation. We went from 200+ data silos to a unified lakehouse that powers everything from customer analytics to network optimization. Our data scientists can now focus on building models instead of searching for data. The churn prediction models alone saved us $67M - multiple times the platform investment."
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