Enterprise-Scale ETL Engine Built for a Major Global Wireless Telecommunications Provider
Transforming massive, scattered data into actionable insights through a high-performance ETL and analytics platform
Automation
Accelerated Operations
Improved Decision-Making
Client Overview
The client is a major global provider of wireless telecommunications services, operating across multiple geographies and serving millions of customers daily. With data flowing continuously from networks, customer platforms, and campaign systems, the organization needed a scalable way to process, analyze, and act on information in near real time.
Sthenos Technologies partnered with the client to design and implement a robust ETL and analytics engine capable of consolidating data from disparate sources and delivering meaningful, decision-ready insights.
Challenge
- Critical data scattered across multiple platforms and channels, making consolidation complex
- Heavy reliance on manual data processing, increasing duplication and error risk
- Limited analytical tooling, restricting the ability to extract accurate, timely insights
- Delayed KPI visibility, slowing operational and strategic decision-making
Our Approach
Sthenos designed the solution with scalability, automation, and analytical depth at its core.
Centralized Data Processing
A unified ETL pipeline was built to ingest, transform, and process large volumes of data from multiple geographic locations and systems.
Automated KPI Computation
Daily KPIs were calculated at a regional level and distributed automatically to stakeholders, eliminating manual reporting cycles.
Insight-Driven Analytics
Processed data was transformed into structured datasets and visual outputs, enabling teams to quickly interpret trends and performance.
API-Driven Data Access
APIs were developed to fetch customer-specific and campaign-level data, supporting targeted analysis and audience segmentation.
Technical Spotlight
- Scalable Data Processing: Python and Java 8 powered data ingestion and transformation logic, supporting high-volume processing.
- Distributed Data Handling: Hadoop enabled scalable processing of large datasets across distributed environments.
- Data Warehousing: Amazon Redshift provided high-performance analytical storage for processed datasets.
- Workflow Orchestration: Apache Airflow managed and automated ETL workflows, ensuring reliability and scheduling consistency.
- Reliable Data Storage: SQL Server supported structured data management and transactional reliability.
- Automated Notifications: Twilio enabled automated alerts and communications for reporting and operational updates.
Solution Delivered
- Ingestion and processing of massive, multi-source datasets
- Automated transformation and aggregation of data into analytics-ready formats
- Daily KPI computation and distribution across regions
- APIs enabling targeted data access for campaigns and business units
Results
- 80% automation of data processing and reporting workflows
- 10× faster operations, reducing time from data ingestion to insight
- 85× improvement in decision-making, driven by timely, accurate analytics
- Reduced duplication, errors, and operational overhead
Tech Stack
Looking to modernize data processing or build high-performance analytics pipelines?
Book a free consultation with Sthenos to explore solutions designed for scale, speed, and actionable insight.