Case study

Advanced Big Data Analytics Platform for a National Telecom Provider

Intellias designed and prototyped a cloud-based data architecture for a leading telecommunications company

Cloud & DevOpsData & AnalyticsTelecom & Media

Project snapshot

A national telecom company was evaluating their options for improving data processing and analytical capabilities by venturing into cloud services. Migrating to the cloud held significant potential for improved business efficiency, cost optimization, enhanced performance, and greater agility. With more than 23 million subscribers, the company was handling hundreds of terabytes of data and considered a cloud computing platform to be a more optimal solution compared to its current on-premises data infrastructure.

Business challenge

The telecom provider chose the Microsoft Azure cloud platform as the base for its data processing architecture. A world leader in cloud computing technologies, Microsoft Azure met all of our client’s requirements, guaranteeing dynamic scalability, multi-layered data security, disaster recovery, and advanced analytical functions.

To ensure smooth migration from on-premises software to the cloud and validate the decision to host data processing infrastructure on Microsoft Azure, the telecom provider needed a solution verifying the advantages of such a migration. And to launch an enterprise-scale data processing infrastructure redesign, the company was looking for a competent technology partner to provide the required cloud migration expertise.

As a Microsoft Gold Partner, Intellias possesses the capabilities and practical experience of setting up cloud solutions for data analytics, particularly for telecommunications companies. Our client trusted the expertise of Intellias engineers to design data infrastructure using Microsoft Azure resources and to orchestrate the eventual migration.

Advanced Big Data Analytics Platform for a National Telecom Provider

Solution delivered

The Intellias team designed the conceptual architecture and developed a phased plan for migrating the client’s infrastructure to the cloud. The design was based on thorough performance benchmarking aimed at finding the most optimal approach to the use of cloud services. The resulting cloud infrastructure combined the features of a data warehouse and a data management platform with powerful business intelligence and analytical capabilities.

We designed a complete data processing and analytics platform based on Microsoft Azure services, which ensured seamless integration and top-notch performance. The infrastructure is capable of ingesting terabytes of telecom data from various sources, preparing data at enterprise scale, and performing complex analytical operations. The design provides for further enhancements of the telecom company’s data activities by enabling machine learning and artificial intelligence functions.

Turn your data potential into value opportunities

Business outcomes

With the conceptual design of cloud infrastructure for data processing, Intellias validated the client’s initiative to leverage the advantages of cloud computing for storing and processing data. The proposed solution meets our client’s main requirements within the context of migrating data processing capacities to the cloud and includes provisions for future machine learning and data science activities:

  • Enhanced business growth through leveraging advanced functionality of a leading cloud computing provider to develop a petabyte-scale data processing infrastructure
  • Total cost of ownership (TCO) optimization and infrastructure cost reduction as a result of using cloud computing resources
  • Tangible performance improvements by migrating the client’s business functions from on-premises to the cloud
  • Advanced analytical features available as a result of implementing cutting-edge technologies
  • Improved fact-based business strategy harnessing the benefits of a multimodal data infrastructure integrating Data Warehousing (DWH) and Data Management Platform (DMP) functionalities
  • Flexible on-demand scalability ensuring the most effective provisioning of computing capacity without compromising performance


times less data processing time achieved with the same hardware


minutes duration of data processing operations instead of the previous 5 hours


reduction in CPU load for data processing operations

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