Course Introduction
Data Engineering with Microsoft Fabric Training Course provides a comprehensive introduction to modern data engineering practices using Microsoft Fabric. The course focuses on how organizations can collect, ingest, transform, store, manage, and prepare data for analytics through an integrated cloud data platform. Participants gain practical knowledge of designing reliable and scalable data engineering solutions.
The course explores key Microsoft Fabric capabilities including OneLake, Lakehouse, Data Factory, notebooks, Spark, SQL, data pipelines, Dataflows Gen2, and Fabric Data Warehouse. Participants learn how these technologies work together to support modern data engineering workflows. The training emphasizes practical techniques for moving data from diverse sources into governed and analytics-ready environments.
Participants will learn how to design data ingestion strategies, build transformation pipelines, process large datasets, and organize analytical data using lakehouse architectures. The course also addresses data quality, schema management, incremental processing, orchestration, monitoring, and performance optimization. These capabilities enable data engineers to build dependable data foundations for reporting, business intelligence, data science, and machine learning.
The training introduces engineering approaches for both batch and near-real-time data processing. Participants examine how modern organizations handle structured, semi-structured, and unstructured data while maintaining efficient and maintainable workflows. Particular attention is given to scalable processing, reusable pipelines, automation, error handling, and operational monitoring within Microsoft Fabric environments.
Emerging data engineering topics are also incorporated throughout the course, including AI-assisted development, intelligent data transformation, real-time analytics, metadata-driven engineering, automation, data governance, and responsible use of enterprise data. Participants explore how evolving technologies are influencing the responsibilities of modern data engineers and how Fabric can support increasingly complex data ecosystems.
By the end of the course, participants will have a structured understanding of how to build end-to-end data engineering solutions with Microsoft Fabric. They will be prepared to work with lakehouses, pipelines, notebooks, Spark, SQL, Dataflows, OneLake, and related Fabric capabilities. The course provides a strong foundation for developing efficient, governed, scalable, and analytics-ready data platforms.
Duration
5 days
Who Should Attend
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Data engineers seeking practical expertise in Microsoft Fabric and modern cloud data engineering.
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ETL developers responsible for designing and maintaining enterprise data integration workflows.
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Database developers transitioning from traditional data platforms to cloud-native engineering environments.
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Data analysts who want to understand the engineering processes behind reliable analytical datasets.
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Business intelligence professionals seeking stronger capabilities in data preparation and pipeline development.
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Cloud engineers responsible for implementing scalable data platforms and integrated analytics environments.
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Microsoft Azure professionals expanding their knowledge of Fabric-based data engineering solutions.
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Data architects designing lakehouse, warehouse, and enterprise data integration architectures.
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Data scientists who require reliable, well-prepared datasets for experimentation and machine learning.
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Power BI professionals interested in understanding upstream data engineering and transformation processes.
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IT professionals supporting enterprise data platforms, integration services, and analytics infrastructure.
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Technical consultants and solution architects implementing modern data engineering solutions for organizations.
Course Objectives
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Explain the architecture and core data engineering capabilities of Microsoft Fabric, including OneLake, Lakehouse, pipelines, notebooks, and warehouse services.
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Design scalable data ingestion solutions that efficiently collect structured, semi-structured, and unstructured information from diverse enterprise data sources.
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Build and manage Fabric Data Factory pipelines for automated data movement, orchestration, transformation, scheduling, and operational monitoring.
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Develop lakehouse solutions using appropriate table, file, schema, and storage structures to support reliable downstream analytical workloads.
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Use notebooks, PySpark, Spark SQL, and related engineering techniques to process, transform, cleanse, and enrich large volumes of enterprise data.
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Implement incremental data processing strategies that reduce unnecessary workloads and improve pipeline efficiency, reliability, and overall processing performance.
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Apply data quality, validation, schema management, and error-handling techniques to create trusted and analytics-ready organizational datasets.
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Integrate Dataflows Gen2, pipelines, lakehouses, warehouses, and other Fabric capabilities into maintainable end-to-end data engineering workflows.
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Apply governance, security, lineage, monitoring, and access-management principles to support responsible and controlled enterprise data engineering.
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Evaluate emerging data engineering capabilities involving AI-assisted development, automation, real-time processing, metadata-driven workflows, and intelligent data transformation.
Comprehensive Course Outline
Module 1: Microsoft Fabric for Data Engineering
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Understanding Microsoft Fabric architecture, core workloads, and the role of data engineering within an integrated analytics platform.
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Exploring OneLake and its role as a unified organizational data foundation for modern engineering and analytical workloads.
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Examining modern data engineering architectures and how Fabric supports ingestion, transformation, storage, orchestration, and analytics.
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Identifying appropriate Fabric services for different data engineering requirements, workloads, sources, processing patterns, and organizational scenarios.
Module 2: OneLake and Lakehouse Architecture
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Creating and organizing Fabric lakehouses using appropriate files, folders, schemas, tables, and analytical storage structures.
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Understanding OneLake shortcuts and data accessibility approaches for reducing unnecessary duplication across enterprise data environments.
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Exploring Delta Lake concepts including transactional tables, schema evolution, versioning, reliability, and analytical data management.
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Designing scalable lakehouse architectures that support data engineering, business intelligence, data science, and downstream analytical workloads.
Module 3: Data Ingestion and Data Factory
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Understanding batch and incremental ingestion patterns for efficiently bringing data from databases, files, applications, APIs, and cloud platforms.
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Building Fabric Data Factory pipelines for repeatable data movement, transformation, orchestration, scheduling, monitoring, and operational control.
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Applying parameters, variables, expressions, dependencies, triggers, and reusable pipeline components to develop maintainable ingestion solutions.
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Addressing emerging ingestion requirements involving APIs, hybrid environments, changing schemas, automation, high-volume sources, and continuously updated information.
Module 4: Data Transformation and Dataflows Gen2
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Using Dataflows Gen2 to perform repeatable data extraction, cleansing, transformation, enrichment, and preparation for analytical consumption.
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Applying practical transformation techniques for handling missing values, inconsistent formats, duplicate records, invalid data, and changing business requirements.
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Designing reusable transformation workflows that improve data consistency, maintainability, automation, and operational efficiency across enterprise environments.
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Exploring emerging low-code and intelligent transformation capabilities that can accelerate engineering workflows while maintaining appropriate data controls.
Module 5: Spark and Notebook-Based Data Engineering
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Understanding Spark architecture and how distributed processing enables scalable transformation and analysis of large enterprise datasets.
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Developing Fabric notebooks using PySpark and Spark SQL to perform advanced data preparation, transformation, aggregation, and engineering tasks.
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Applying partitioning, caching, joins, file management, and processing strategies to improve Spark workload efficiency and scalability.
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Exploring emerging AI-assisted coding and intelligent development capabilities that can support data engineers during notebook and transformation development.
Module 6: Advanced Lakehouse Engineering
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Designing reliable lakehouse tables and structures that support efficient analytical querying, data reuse, governance, and downstream reporting requirements.
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Implementing medallion-style data architectures to organize raw, refined, and business-ready datasets within modern engineering environments.
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Applying schema evolution, data validation, deduplication, incremental loading, and historical data management techniques for dependable pipelines.
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Addressing challenges involving growing data volumes, complex dependencies, changing schemas, storage efficiency, and long-term lakehouse maintainability.
Module 7: Fabric Data Warehouse and SQL Engineering
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Understanding the role of Fabric Data Warehouse in structured analytical workloads and enterprise SQL-based data engineering solutions.
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Creating tables, views, procedures, queries, and analytical structures using SQL for organized and reusable enterprise data processing.
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Applying dimensional modeling concepts to prepare reliable datasets for reporting, business intelligence, analytics, and decision-support applications.
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Comparing lakehouse and warehouse engineering patterns while identifying suitable approaches based on workload, performance, governance, and business requirements.
Module 8: Pipeline Orchestration, Monitoring, and Performance
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Designing orchestrated data workflows with dependencies, scheduling, triggers, parameters, monitoring, and appropriate operational controls.
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Implementing error handling, retries, logging, notifications, and validation processes to improve pipeline reliability and operational resilience.
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Applying performance optimization techniques across pipelines, Spark workloads, lakehouses, SQL queries, storage structures, and data transformations.
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Exploring metadata-driven and automated engineering approaches that can improve scalability, reduce repetitive development, and simplify enterprise pipeline management.
Module 9: Data Quality, Governance, and Security
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Implementing data quality practices covering validation, completeness, consistency, accuracy, duplication, schema control, and trustworthy data delivery.
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Understanding data lineage and governance concepts that help organizations track data origins, transformations, dependencies, ownership, and consumption.
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Applying security principles involving workspace permissions, access control, authentication, sensitive data management, and appropriate engineering responsibilities.
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Examining emerging governance challenges involving AI-generated data workflows, privacy requirements, regulatory compliance, cross-platform integration, and responsible automation.
Module 10: Real-Time Engineering, AI, and Emerging Trends
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Understanding real-time data engineering concepts and identifying appropriate approaches for event-driven, streaming, operational, and continuously updated workloads.
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Exploring Fabric capabilities for integrating real-time information with traditional batch pipelines, lakehouses, warehouses, and analytical applications.
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Examining emerging AI, machine learning, automation, intelligent development, and metadata-driven technologies that are changing modern data engineering practices.
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Developing an end-to-end Fabric data engineering solution integrating ingestion, transformation, storage, orchestration, monitoring, governance, and analytics readiness.
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.
