Course Introduction
Azure Data Analytics with Synapse and Fabric Training Course provides a practical foundation for professionals who need to design, build, manage, and optimize modern analytics solutions across Azure Synapse Analytics and Microsoft Fabric. The course connects enterprise data warehousing, big data processing, data integration, analytics, and business intelligence into a cohesive learning experience.
Azure Synapse Analytics brings together SQL technologies, Apache Spark, Data Explorer, pipelines, and data lake capabilities within an integrated analytics environment. Microsoft Fabric extends modern analytics with an end-to-end platform covering data ingestion, transformation, real-time processing, analytics, data warehousing, and reporting. This course explores how these capabilities can support different analytics requirements and solution architectures.
Participants learn how to work with analytical data using Synapse SQL, serverless and dedicated SQL capabilities, Apache Spark, data lakes, and integrated pipelines. The course also introduces Fabric concepts such as OneLake, Lakehouse, Data Warehouse, Data Factory, semantic models, and Power BI integration. Microsoft describes Fabric as a unified platform in which multiple analytics workloads operate over shared storage and compute experiences.
The training emphasizes practical data analytics workflows, including ingesting data from multiple sources, transforming and preparing datasets, querying large-scale data, creating analytical models, and producing business-ready insights. Participants examine how Synapse and Fabric can work with existing Azure data environments and how Fabric Data Factory can connect with Azure Synapse Analytics for data movement and transformation scenarios.
Security, governance, performance, and operational considerations are incorporated throughout the course rather than treated as separate afterthoughts. Learners explore workspace management, access control, data protection, workload monitoring, query optimization, capacity considerations, and efficient pipeline design. These practices help organizations build analytics environments that are reliable, scalable, secure, and manageable as data volumes and business requirements increase.
By completing the course, participants will be better prepared to evaluate analytics requirements, select suitable Synapse and Fabric capabilities, develop integrated analytical workflows, and communicate insights through modern BI solutions. The learning path is suitable for professionals transitioning toward cloud analytics as well as experienced Azure data practitioners seeking practical knowledge of the evolving Microsoft analytics ecosystem.
Duration
5 days
Who Should Attend
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Data analysts responsible for preparing, querying, and interpreting enterprise datasets.
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Business intelligence professionals developing analytical reports and dashboards.
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Data engineers building ingestion, transformation, and analytics pipelines.
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Azure data professionals working with Synapse Analytics and related services.
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Database professionals moving from traditional data platforms to cloud analytics.
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BI developers integrating analytical datasets with Power BI and semantic models.
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Analytics engineers designing scalable data models and transformation workflows.
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Cloud architects evaluating Microsoft analytics platform architectures.
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Data warehouse developers working with SQL-based analytical workloads.
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Technical consultants implementing Azure-based data and analytics solutions.
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IT professionals supporting enterprise data platforms and analytics environments.
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Professionals preparing for Microsoft Azure data and analytics responsibilities.
Course Objectives
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Understand the architecture, capabilities, and use cases of Azure Synapse Analytics and Microsoft Fabric for modern enterprise data analytics.
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Develop practical skills for ingesting, transforming, querying, and analyzing structured and semi-structured data across cloud analytics environments.
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Use Synapse SQL capabilities to perform analytical queries and understand appropriate approaches for serverless and dedicated workloads.
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Explore Apache Spark within Synapse and Fabric to support large-scale data preparation, transformation, exploration, and analytical processing.
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Design data pipelines that integrate multiple sources and automate movement and transformation of data for downstream analytics.
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Understand Microsoft Fabric architecture, including OneLake, Lakehouse, Data Warehouse, Data Factory, semantic models, and Power BI integration.
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Build analytical solutions that connect Azure Synapse environments with Microsoft Fabric using supported data integration and connectivity approaches.
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Apply data modeling, performance optimization, security, governance, and operational practices to improve analytics solution quality.
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Create business-ready analytical outputs by connecting prepared datasets and semantic models to reporting and visualization capabilities.
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Evaluate real-world analytics requirements and select suitable Synapse and Fabric technologies based on workload, scalability, performance, governance, and integration needs.
Comprehensive Course Outline
Module 1: Azure Analytics and Microsoft Fabric Fundamentals
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Overview of modern Azure analytics architectures and the role of cloud data platforms in enterprise decision-making.
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Core capabilities, use cases, and architectural concepts of Azure Synapse Analytics and Microsoft Fabric.
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Comparison of Synapse SQL, Spark, Data Explorer, Fabric Lakehouse, Fabric Warehouse, and related analytics workloads.
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Understanding how integrated Microsoft analytics services support end-to-end data ingestion, processing, analysis, and reporting.
Module 2: Azure Synapse Analytics Architecture and Workspace
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Creating and configuring Azure Synapse workspaces and understanding the major components available within the environment.
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Exploring Synapse SQL, Apache Spark, Data Explorer, pipelines, storage integration, monitoring, and administrative capabilities.
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Understanding workspace security, resource organization, linked services, integration runtimes, and connectivity to Azure data sources.
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Navigating Synapse Studio to develop, execute, monitor, and manage analytical workloads across multiple data services.
Module 3: Synapse SQL and Enterprise Data Warehousing
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Working with Synapse SQL and T-SQL to query, transform, and analyze enterprise-scale analytical datasets.
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Understanding serverless SQL pools for querying data without maintaining dedicated data warehouse compute resources.
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Exploring dedicated SQL pools, distributed processing concepts, table design, data distribution, and analytical workload considerations.
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Applying SQL techniques for aggregations, joins, filtering, window functions, data quality checks, and analytical reporting.
Module 4: Data Lakes, Storage, and Apache Spark Analytics
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Understanding Azure data lake concepts and how analytical data can be organized for scalable processing and exploration.
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Using Apache Spark notebooks to read, transform, analyze, and write large-scale datasets across supported storage environments.
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Working with structured and semi-structured data formats and applying appropriate transformation techniques for analytics.
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Comparing SQL-based and Spark-based processing approaches and selecting suitable engines for different analytical requirements.
Module 5: Data Integration and Pipeline Development
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Designing data ingestion workflows that move information from operational, cloud, file-based, and enterprise data sources.
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Building pipelines to orchestrate data movement, transformation, validation, scheduling, dependencies, and downstream processing.
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Applying incremental loading, parameterization, error handling, monitoring, and reusable pipeline design patterns.
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Understanding Fabric Data Factory capabilities for integrating data from Azure Synapse Analytics and other supported sources.
Module 6: Microsoft Fabric Analytics Platform
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Exploring Microsoft Fabric architecture and understanding how integrated workloads support comprehensive enterprise analytics.
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Working with OneLake as the centralized logical data lake and understanding how Fabric workloads access shared organizational data.
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Exploring Fabric Lakehouse, Data Warehouse, Data Factory, Data Engineering, Real-Time Intelligence, and Power BI capabilities.
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Understanding how Fabric enables integrated analytics workflows while reducing the need to manually connect separate services.
Module 7: Fabric Data Modeling, Lakehouse, and Warehouse Analytics
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Creating and working with Lakehouse structures for storing, preparing, and analyzing analytical data in Microsoft Fabric.
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Exploring Fabric Data Warehouse concepts, SQL querying, table structures, relationships, and analytical workload design.
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Developing effective data models that support reusable analysis, reporting, performance, and consistent business definitions.
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Connecting analytical datasets and semantic models to downstream reporting and business intelligence solutions.
Module 8: Power BI, Semantic Models, and Business Analytics
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Preparing analytical datasets and semantic models for interactive business intelligence and self-service analytics.
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Connecting Synapse and Fabric data solutions with Power BI to create dashboards, reports, visualizations, and analytical experiences.
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Applying filtering, measures, calculated logic, relationships, and modeling techniques to support meaningful business analysis.
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Designing clear analytical reports that transform complex datasets into actionable business information.
Module 9: Performance, Security, Governance, and Monitoring
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Applying query optimization, workload management, data modeling, and processing techniques to improve analytical performance.
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Understanding shared capacity, resource utilization, workload isolation, and performance considerations within Fabric environments.
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Implementing access controls, workspace security, governance practices, auditing, and appropriate protection for enterprise data.
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Monitoring pipelines, queries, workloads, and operational activity to identify issues and maintain reliable analytics services.
Module 10: End-to-End Azure Synapse and Fabric Analytics Solution
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Designing an integrated analytics solution that combines data ingestion, transformation, storage, processing, modeling, and visualization.
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Building an end-to-end scenario that moves data through Synapse or Fabric and produces business-ready analytical insights.
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Evaluating architecture decisions based on scalability, performance, security, governance, maintainability, and organizational requirements.
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Applying course concepts to practical enterprise analytics scenarios and developing a roadmap for modernizing existing data environments.
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.
