From Traditional DWH to Apache Doris: Building a Real-Time Telecom Lakehouse for Analytics at Scale

About this session

Telecom operators generate massive volumes of data from CDRs, mobile money transactions, network events, customer interactions, and operational systems. Traditional data warehouse architectures can struggle to deliver the scalability, flexibility, and real-time performance required for modern analytics. This session shares the practical journey of transforming a traditional Oracle-based telecom reporting environment into a modern Apache Doris-powered real-time lakehouse. It explores how Apache Doris is integrated with technologies such as Kafka, Flink, Hadoop, and BSS/OSS databases to build a scalable platform capable of supporting real-time ingestion, operational analytics, BI, and advanced analytics workloads. The session will walk through the end-to-end architecture—from data ingestion and streaming pipelines through Doris data modeling, optimization, reconciliation, and consumption by BI platforms. It will also highlight real production challenges. Attendees will gain practical insights into migrating from a traditional data warehouse to Apache Doris, designing a real-time telecom lakehouse, optimizing Doris for high-volume analytical workloads, and building a platform that can evolve from traditional BI toward AI and machine-learning use cases.

Speaker

James Mandiza

Senior Manager, Data Platforms, Analytics and AI · Telekom Networks Malawi

I am James Mandiza, a Senior Manager in Data Platforms, Analytics and AI at Telekom Networks Malawi (TNM), with over 15 years of experience in telecommunications, data analytics, business intelligence, and technology. My work focuses on building modern data platforms, real-time analytics solutions, and AI-driven applications that turn large volumes of telecom data into actionable business insights. I have been involved in transforming traditional data warehouse environments into modern lakehouse architectures using technologies including Apache Doris, Apache Kafka, Apache Flink, Apache Iceberg, and Hadoop. At TNM, I have led initiatives covering real-time data processing, enterprise BI, customer analytics, revenue assurance, mobile money analytics, and machine learning. I am particularly passionate about using open-source technologies to build scalable, high-performance data platforms that support both operational and advanced analytics.

LinkedIn ↗ · James Mandiza