Developing SQL 2016 Data Models (SSAS) Course Overview

Developing SQL 2016 Data Models (SSAS) Course Overview

The Developing SQL 2016 Data Models (SSAS) course is designed to provide learners with a comprehensive understanding and practical skill set for implementing data models using Microsoft SQL Server Analysis Services (MS SSAS). This course covers various aspects of both multidimensional and tabular data models, diving into the creation and management of cubes, dimensions, and measures. Learners will also explore the essentials of the Multidimensional Expressions (MDX) language and the Data Analysis Expressions (DAX) language for more complex data manipulation and analysis.

Throughout the course, students will gain practical experience with MS SSAS through a series of labs that reinforce the lessons, enabling them to configure dimensions, measure groups, and implement security within their data models. Additionally, learners will delve into predictive analytics with data mining, a key feature of MS SQL Server Analysis Services. By the end of the course, participants will have the skills to build robust, efficient data models that are essential for enterprise-level business intelligence solutions.

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  • Live Online Training (Duration : 24 Hours)
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♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

To ensure that participants are able to successfully undertake the Developing SQL 2016 Data Models (SSAS) course, the following are the minimum required prerequisites:

  • Basic understanding of data warehouse schema topology (including star and snowflake schemas).
  • Some exposure to basic concepts of relational databases, such as tables and relationships.
  • Familiarity with Microsoft Windows and its core functionality.
  • Prior experience with Microsoft SQL Server or other database management systems.
  • Basic knowledge of SQL, including how to create and manage database objects.
  • An understanding of basic programming constructs (such as loops and conditionals) is helpful but not mandatory.

Please note that these prerequisites are intended to provide a foundation for the course content and to facilitate a productive learning experience. However, instructors will provide comprehensive guidance and support throughout the training to accommodate varying skill levels among participants.

Target Audience for Developing SQL 2016 Data Models (SSAS)

The "Developing SQL 2016 Data Models" course caters to professionals aiming to enhance their BI and data modeling skills.

  • Business Intelligence (BI) Analysts
  • Data Analysts
  • Database Administrators
  • Database Developers
  • Data Architects
  • Data Scientists
  • IT Professionals working with SQL Server
  • SQL Server Developers
  • Business Analysts involved in BI projects
  • System Architects
  • BI Developers
  • Report Developers
  • Professionals preparing for Microsoft certification exams related to SQL Server Analysis Services (SSAS)

Learning Objectives - What you will Learn in this Developing SQL 2016 Data Models (SSAS)?

Introduction to Course Learning Outcomes:

Gain expertise in SQL 2016 Data Models with a focus on building business intelligence solutions, multidimensional databases, and tabular data models, incorporating MDX, DAX, and data mining techniques.

Learning Objectives and Outcomes:

  • Understand the fundamentals of business intelligence and the Microsoft BI platform.
  • Learn to create and manage multidimensional structures such as data sources, data source views, and cubes.
  • Configure dimensions, attributes, and measure groups for accurate data analysis.
  • Implement cube security to protect sensitive business information.
  • Develop proficiency in Multidimensional Expressions (MDX) for adding calculations and querying cubes.
  • Customize cubes with advanced features like key performance indicators (KPIs), actions, perspectives, and translations.
  • Construct and deploy tabular data models using Analysis Services to enhance enterprise BI solutions.
  • Apply Data Analysis Expressions (DAX) to create calculated columns and measures within tabular models.
  • Perform predictive analysis by employing data mining techniques and validate the accuracy of data mining models.
  • Integrate and consume data mining models in business scenarios for informed decision-making.