Course Information
Course Name: Microsoft SQL Server 2019 Analysis Services (SSAS) Online Course
Total Video Hours: 8 Hrs 56 Min
Total Videos: 50
Delivery Mode: Online, instructor-led recorded sessions
Skill Level: Intermediate to Advanced
Focus Area: Business Intelligence, OLAP, Tabular Models, Data Mining
Included in This Course
Structured video-based training with real-world BI scenarios
Practical demonstrations using Microsoft SQL Server 2019 Analysis Services
Coverage of both Multidimensional and Tabular data models
MDX and DAX query language fundamentals
Cube customization using KPIs, actions, perspectives, and translations
Data mining concepts with validation and consumption techniques
Enterprise-level BI modeling and analytics workflows
Course Outline
Module 1: Introduction to Business Intelligence and Data Modeling
Module 2: Multidimensional Databases
Module 3: Cubes and Dimensions
Module 4: Measures and Measure Groups
Module 5: Introduction to MDX
Module 6: Customizing Cube Functionality
Module 7: Tabular Data Models
Module 8: Data Analysis Expressions (DAX)
Module 9: Data Mining
Understanding the Role of SSAS in Enterprise Business Intelligence
Microsoft SQL Server Analysis Services plays a central role in enterprise business intelligence solutions by providing semantic data modeling, advanced analytics, and scalable performance for reporting systems. Organizations rely on SSAS to transform raw transactional data into structured analytical models that support strategic decision-making. This course addresses both analytical engines available in SQL Server 2019: VertiPaq for multidimensional cubes and xVelocity for tabular models, ensuring balanced technical coverage aligned with modern BI requirements.
Business Intelligence Foundations and Data Modeling Concepts
Business intelligence systems depend on well-designed data models that reflect organizational processes and performance indicators. The initial part of the course establishes BI fundamentals, including data warehouses, data models, and the Microsoft BI platform architecture. Emphasis is placed on understanding how data flows from source systems into analytical models and how these models support reporting tools such as Power BI, Excel, and SQL Server Reporting Services.
Clear explanations of data warehouses and semantic layers help learners connect operational data with analytical outcomes. These concepts provide the foundation for building scalable and reliable SSAS solutions.
Multidimensional Databases and OLAP Architecture
Multidimensional databases form the backbone of traditional OLAP solutions. This section explains cube architecture, data sources, and data source views used within SQL Server Analysis Services. Security concepts are introduced to ensure controlled access to enterprise data. Cube creation and configuration are demonstrated in a structured manner, allowing learners to understand how analytical dimensions and measures are organized for efficient querying and reporting.
Design considerations such as performance, storage modes, and enterprise deployment scenarios are integrated throughout the discussion.
Cubes, Dimensions, and Hierarchies
Dimensions define how data is analyzed across different perspectives such as time, geography, or product categories. This course section focuses on dimension design, attribute hierarchies, relationships, sorting, and grouping techniques. Slowly changing dimensions are explained to address real-world data evolution challenges. These topics enable accurate historical analysis while maintaining data consistency.
Understanding dimension behavior is critical for delivering reliable analytical insights, and this training emphasizes best practices used in enterprise BI environments.
Measures and Measure Groups for Analytical Accuracy
Measures represent quantitative values such as sales, costs, or performance metrics. The course explains how measures are created, organized into measure groups, and linked to dimensions. Storage options and relationship types are discussed to help optimize query performance and scalability.
By addressing how measures interact with dimensional data, learners gain the ability to design analytical models that deliver accurate, consistent, and high-performance results across reporting platforms.
MDX Fundamentals for Multidimensional Querying
Multidimensional Expressions (MDX) remains essential for querying OLAP cubes. This section introduces MDX syntax, cube navigation, and calculated members. Practical examples demonstrate how MDX retrieves and manipulates data within cubes, supporting advanced analytical queries beyond standard reporting tools.
Adding calculations and custom logic using MDX enhances analytical flexibility, enabling organizations to answer complex business questions efficiently.
Customizing Cube Functionality for Business Requirements
Customization features in SSAS allow analytical models to align closely with business objectives. This course covers key performance indicators (KPIs), actions, perspectives, and language translations. These elements improve usability, user experience, and global accessibility of analytical solutions.
Through customization, analytical models become more intuitive for business users while maintaining technical integrity and performance.
Tabular Data Models and Modern BI Solutions
Tabular data models offer a flexible, in-memory approach to analytics using xVelocity technology. This section explains the structure, benefits, and use cases of tabular models compared to multidimensional cubes. Learners gain hands-on understanding of model creation, relationship configuration, and enterprise BI deployment considerations.
Tabular models are widely used with Power BI and modern reporting tools, making this knowledge highly relevant for current BI environments.
Data Analysis Expressions (DAX) for Advanced Calculations
DAX is a powerful expression language used in tabular models for calculations, measures, and time intelligence. This course provides structured coverage of DAX fundamentals, calculated columns, relationships, KPIs, and parent-child hierarchies. Practical scenarios demonstrate how DAX enhances analytical depth and reporting accuracy.
Mastery of DAX enables professionals to design responsive and intelligent BI solutions aligned with organizational metrics.
Data Mining and Analytical Pattern Recognition
Data mining extends business intelligence into predictive and pattern-based analysis. This section introduces data mining concepts within SQL Server Analysis Services, including model creation, validation, and consumption. Learners examine how analytical models uncover trends, correlations, and hidden insights within large datasets.
The course explains how to evaluate multiple data mining models and select appropriate techniques for specific business scenarios, supporting informed decision-making.
Enterprise Application and Professional Outcomes
Microsoft SQL Server 2019 Analysis Services skills are essential for BI developers, data analysts, and database professionals involved in enterprise reporting systems. This course supports roles requiring advanced analytical modeling, performance optimization, and data-driven strategy development.
Organizations benefit from improved reporting accuracy, faster insights, and scalable analytics infrastructure when SSAS solutions are properly designed and implemented.
Frequently Asked Questions
Who should enroll in this Microsoft SQL Server 2019 Analysis Services (SSAS) Online Course?
This course is suitable for BI developers, data analysts, database professionals, and IT specialists working with enterprise analytics and reporting systems.
Is prior SQL Server experience required for this SSAS training?
Basic knowledge of SQL Server and database concepts is recommended to fully understand analytical modeling and query techniques.
Does this course cover both multidimensional and tabular models?
Yes, the training includes detailed coverage of both multidimensional cubes and tabular data models used in SQL Server 2019 Analysis Services.
Are MDX and DAX both included in the course content?
The course provides structured instruction on MDX for multidimensional models and DAX for tabular data models.
Is data mining included as part of the SSAS curriculum?
Yes, the course includes data mining concepts, model creation, validation, and consumption techniques.
Can this course support enterprise BI implementation projects?
The content aligns with real-world enterprise BI scenarios and supports professional implementation requirements.
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