Course Introduction
The Telecommunications Network Data Engineering Training Course provides comprehensive practical knowledge for designing, integrating, processing, transforming, storing, governing, and analyzing the large volumes of data generated by modern telecommunications networks. The course focuses on the engineering practices required to turn network events, performance measurements, subscriber information, usage records, alarms, topology data, and operational telemetry into reliable datasets for network operations, optimization, service assurance, and business analytics.
Participants explore the complex data ecosystems generated by mobile, fixed-line, broadband, wireless, enterprise, and converged telecommunications environments. The training examines data originating from network elements, radio access networks, core networks, transmission systems, service platforms, customer systems, network management applications, probes, billing platforms, and operational support systems. Learners gain an understanding of how these diverse sources can be integrated into scalable data platforms.
A major focus is placed on high-volume network data ingestion and real-time processing. Telecommunications networks generate continuously changing streams of events, counters, alarms, usage records, and performance measurements. The course examines message brokers, streaming architectures, batch processing, event-time processing, data partitioning, windowing, aggregation, filtering, enrichment, and distributed processing techniques required to manage this scale while maintaining reliable and timely information delivery.
The training also addresses the specialized data quality challenges found in telecommunications environments. Participants learn how to handle duplicate events, missing measurements, delayed records, inconsistent identifiers, changing network configurations, timestamp discrepancies, incomplete usage records, and evolving schemas. Practical approaches to validation, normalization, reconciliation, enrichment, anomaly identification, and data-quality monitoring are incorporated to ensure that downstream operational and analytical systems receive dependable information.
The course further explores network performance, service assurance, customer experience, and operational analytics. Learners examine how engineered network datasets can support key performance indicator analysis, capacity planning, fault management, traffic analysis, network optimization, service monitoring, and customer experience assessment. The training connects data engineering practices with operational telecommunications requirements while maintaining a strong emphasis on scalable and reliable data platforms.
Security, governance, observability, and platform reliability are integrated throughout the course. Participants examine access controls, encryption, data classification, metadata, lineage, retention, auditing, secure integration, pipeline monitoring, and operational recovery. By completing the Telecommunications Network Data Engineering Training Course, participants will be better prepared to design robust data platforms capable of supporting high-volume network environments, real-time operational intelligence, historical analysis, and evolving telecommunications technology ecosystems.
Duration
10 days
Who Should Attend
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Telecommunications data engineers responsible for network data ingestion, processing, integration, and analytical pipelines.
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Network engineers seeking stronger capabilities in network telemetry and operational data engineering.
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OSS and BSS professionals working with operational support and business support system data.
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Telecom architects designing scalable network data platforms and analytics architectures.
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Network performance engineers analyzing KPIs, counters, events, alarms, and service-quality information.
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Cloud engineers implementing telecommunications data platforms across cloud, on-premises, and hybrid environments.
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Data architects designing network data lakes, warehouses, streaming platforms, and analytical repositories.
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Software developers building applications that consume network events, telemetry, usage records, or operational data.
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Telecom operations professionals working with network monitoring, fault management, capacity planning, and service assurance.
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Database professionals managing high-volume telecommunications datasets and time-series network information.
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Analytics engineers preparing network and subscriber datasets for operational and business intelligence.
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Data governance professionals responsible for telecom data quality, lineage, security, classification, and lifecycle management.
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Technical managers overseeing network modernization, data platform development, automation, or telecom transformation initiatives.
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Professionals working with mobile networks, broadband networks, fixed telecommunications, enterprise connectivity, or converged network environments.
Course Objectives
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Understand telecommunications data ecosystems and how network events, performance measurements, alarms, usage records, topology information, and subscriber data move through operational environments.
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Design scalable data engineering architectures capable of processing high-volume and high-velocity telecommunications data from distributed network elements and operational systems.
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Build reliable ingestion pipelines for network telemetry, performance counters, alarms, usage records, logs, APIs, databases, files, and streaming event sources.
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Apply batch and real-time streaming techniques to process telecommunications events with appropriate latency, throughput, ordering, partitioning, retention, and recovery characteristics.
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Develop data models for network elements, cells, sites, interfaces, subscribers, services, sessions, events, alarms, measurements, and other important telecommunications entities.
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Implement data quality controls that identify missing measurements, duplicate events, inconsistent identifiers, invalid values, timestamp discrepancies, and network configuration changes.
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Transform and enrich network datasets by combining telemetry with topology, configuration, geographic, subscriber, service, and operational reference information.
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Build analytical data pipelines that support network performance management, service assurance, capacity planning, traffic analysis, fault management, and customer experience monitoring.
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Apply security and governance practices for protecting sensitive network, subscriber, operational, and business information throughout the data lifecycle.
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Implement observability mechanisms for monitoring ingestion throughput, pipeline latency, data freshness, processing failures, network-source availability, and analytical dataset quality.
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Design resilient telecommunications data platforms using partitioning, buffering, checkpointing, replay, fault recovery, redundancy, and scalable distributed processing approaches.
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Develop an end-to-end understanding of telecommunications network data engineering that supports modern network operations, automation, analytics, optimization, and technology evolution.
Comprehensive Course Outline
Module 1: Foundations of Telecommunications Network Data Engineering
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Understanding telecommunications data ecosystems and the relationships among network elements, management systems, operational platforms, subscribers, services, and analytical environments.
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Exploring major categories of network data including performance counters, alarms, events, logs, usage records, topology data, configuration information, and service measurements.
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Examining the characteristics of telecom data such as high volume, high velocity, continuous generation, temporal dependencies, distributed sources, and rapidly changing network configurations.
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Identifying common data engineering use cases including network monitoring, performance management, service assurance, capacity planning, fault analysis, optimization, and customer experience analytics.
Module 2: Telecommunications Network Architecture and Data Sources
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Exploring mobile, fixed, broadband, wireless, enterprise, and converged network architectures and the data generated by their major network components.
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Understanding data sources from radio access, core, transport, access, service, network management, and operational support environments.
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Mapping network elements, management platforms, databases, interfaces, probes, and operational applications to determine appropriate data ingestion strategies.
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Examining how network modernization and evolving technologies influence telemetry formats, data volumes, event structures, and integration requirements.
Module 3: Network Telemetry and Data Collection
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Understanding network telemetry, counters, measurements, logs, alarms, events, traces, and other operational information generated by telecommunications infrastructure.
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Designing collection strategies that account for polling intervals, event frequency, data granularity, source capabilities, bandwidth, storage requirements, and operational priorities.
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Implementing reliable collection processes for distributed network elements while handling temporary connectivity issues, source failures, and incomplete observations.
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Managing telemetry metadata such as network-element identifiers, timestamps, geographic locations, technology types, measurement units, and configuration context.
Module 4: Telecom Data Ingestion and Integration
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Designing scalable ingestion pipelines for databases, APIs, network management systems, files, message queues, event streams, and other telecommunications data sources.
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Implementing ingestion processes that preserve raw network events, source identifiers, timestamps, sequence information, and processing metadata for traceability.
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Applying batch, micro-batch, and streaming ingestion patterns according to network data characteristics, operational latency requirements, and source-system capabilities.
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Developing failure handling, retry, buffering, checkpointing, and recovery mechanisms that maintain data continuity during network or platform interruptions.
Module 5: Streaming Data and Event Processing
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Understanding event-streaming architectures for processing alarms, network events, performance measurements, usage records, and service-status changes in near real time.
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Designing topics, partitions, schemas, consumer patterns, retention strategies, and delivery approaches for large-scale telecommunications event streams.
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Applying event-time processing, windowing, filtering, aggregation, enrichment, and correlation techniques to continuously generated network information.
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Managing event ordering, duplicate messages, delayed events, replay, backpressure, and fault recovery within high-volume telecom streaming environments.
Module 6: Network Data Modeling and Time-Series Engineering
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Designing data models for network elements, cells, sites, interfaces, sessions, subscribers, services, alarms, measurements, and other telecommunications entities.
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Understanding time-series modeling requirements for performance counters, network measurements, traffic statistics, and continuously generated operational metrics.
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Applying partitioning, indexing, compression, retention, aggregation, and storage strategies for high-frequency telecommunications measurements.
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Connecting time-series measurements with network topology, configuration, geography, service information, and operational metadata to create useful analytical datasets.
Module 7: Network Data Quality and Transformation
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Identifying data quality problems such as missing counters, duplicate events, invalid measurements, inconsistent units, timestamp errors, and incomplete network records.
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Implementing validation rules that compare telemetry values against expected ranges, source specifications, historical behavior, network configurations, and operational context.
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Applying normalization and transformation techniques to standardize identifiers, measurement units, timestamps, technology labels, network locations, and event classifications.
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Establishing data quality metrics, exception workflows, and monitoring processes that provide visibility into source reliability and downstream dataset accuracy.
Module 8: Network Topology, Configuration, and Reference Data
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Understanding network topology data and how relationships among sites, cells, network elements, interfaces, links, services, and geographic locations support operational analysis.
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Integrating configuration management information with telemetry and performance data to provide context for network measurements and operational events.
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Designing reference-data structures for network technologies, equipment types, vendors, geographic regions, service categories, and operational classifications.
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Managing topology and configuration changes while maintaining historical relationships and ensuring analytical datasets reflect appropriate network states over time.
Module 9: Network Performance and KPI Data Engineering
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Designing pipelines for network performance indicators covering availability, utilization, traffic, latency, throughput, accessibility, retainability, and other operational measurements.
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Transforming raw network counters into standardized KPIs through aggregation, normalization, calculation, enrichment, and validation workflows.
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Developing historical performance datasets that support trend analysis, benchmarking, capacity planning, optimization, and network investment decisions.
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Ensuring KPI pipelines maintain consistent definitions, calculation logic, metadata, lineage, and traceability to underlying measurements and source systems.
Module 10: Fault, Alarm, and Service Assurance Data Engineering
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Designing data pipelines that ingest, normalize, correlate, and analyze alarms and network events generated by telecommunications infrastructure.
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Implementing event correlation approaches that combine alarms, topology, configuration, performance measurements, and service information to improve operational context.
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Developing real-time processing workflows for identifying recurring events, alarm patterns, service-impacting conditions, and infrastructure anomalies.
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Managing alarm lifecycle information including creation, updates, acknowledgements, clearing, correlation, escalation, and historical analysis.
Module 11: Subscriber, Usage, and Service Data Engineering
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Designing data models and pipelines for subscriber activity, service usage, session information, traffic records, and other authorized telecommunications business data.
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Integrating network usage information with service, customer, geographic, and product reference data while maintaining appropriate privacy and access controls.
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Processing high-volume usage records through batch and distributed pipelines for operational reporting, service analysis, capacity planning, and business intelligence.
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Implementing validation and reconciliation techniques to identify missing usage records, duplicate records, inconsistent identifiers, and unexpected changes in traffic patterns.
Module 12: Telecom Data Security, Governance, and Privacy
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Understanding security and privacy considerations associated with network telemetry, subscriber information, operational data, usage records, and telecommunications infrastructure information.
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Implementing authentication, authorization, encryption, access controls, secrets management, audit logging, and secure data-transfer mechanisms across network data platforms.
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Establishing governance practices for data ownership, classification, metadata, lineage, retention, quality, access management, and controlled sharing.
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Designing security-aware data architectures that balance operational accessibility with appropriate protection for sensitive network and subscriber information.
Module 13: Network Analytics and Operational Intelligence
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Preparing integrated network datasets for operational dashboards, performance analysis, capacity planning, service assurance, optimization, and engineering decision support.
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Designing analytical models that organize information by network element, site, cell, technology, service, geography, time period, and operational domain.
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Building data products that combine performance measurements, alarms, topology, configuration, and usage information to provide broader operational context.
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Supporting historical and near-real-time analytics while maintaining consistent definitions, documented transformations, data lineage, and quality indicators.
Module 14: Telecom Cloud and Distributed Data Platforms
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Designing cloud and hybrid architectures capable of processing large telecommunications datasets while meeting latency, scalability, security, availability, and operational requirements.
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Integrating streaming platforms, object storage, analytical warehouses, databases, orchestration services, APIs, and observability tools into cohesive telecom data platforms.
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Applying distributed processing strategies for high-volume telemetry, performance data, event streams, and usage records across multiple network domains.
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Evaluating platform architecture choices based on throughput, resilience, operational complexity, cost, data locality, connectivity, and evolving network requirements.
Module 15: Data Pipeline Observability and Network Data Operations
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Monitoring network data pipelines for throughput, processing latency, source availability, data freshness, event loss, schema changes, and infrastructure utilization.
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Implementing logs, metrics, traces, alerts, dashboards, and audit records that provide visibility into distributed telecommunications data processing.
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Designing retry, dead-letter, checkpointing, replay, failover, buffering, and recovery strategies for critical network data pipelines.
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Establishing operational procedures for troubleshooting source failures, missing telemetry, transformation problems, delayed events, processing backlogs, and analytical data discrepancies.
Module 16: End-to-End Telecommunications Data Engineering Project
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Designing a complete telecommunications data platform covering network telemetry ingestion, event streaming, transformation, time-series storage, topology integration, security, monitoring, and analytics.
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Implementing an end-to-end pipeline that processes realistic network data while addressing high event volumes, duplicate records, delayed measurements, schema changes, and operational failures.
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Developing network performance dashboards, alarm analytics, service monitoring datasets, or operational data products that demonstrate the practical value of engineered telecommunications information.
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Testing the completed solution for data quality, scalability, latency, resilience, security, observability, recovery capability, and end-to-end traceability.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
