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With this course, you will be able to become an expert in employing both engines within a company’s infrastructure. Analysis Services is composed of two powerful applications – VertiPaq and Data Mining for multidimensional cubes, as well as xVelocity for Tabular Data Models. It provides enterprise-level semantic data modeling abilities to construct business intelligence systems such as Power BI, Excel Reporting Services and other information visualization tools that can make your organization even more successful!
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Course Description
Unlock the power of data analytics with our comprehensive course on Microsoft SQL Server Analysis Services, SSAS. Whether you’re new to SSAS or looking to deepen your understanding, this course covers everything from the basics to advanced topics. Learn what SSAS means in the context of business intelligence, master the art of creating SSAS cubes, and explore the SSAS tabular model to make data-driven decisions.
Kickstart your journey into the world of business intelligence and data modeling with Module 1. This module serves as an introduction to what SSAS is and how it fits into the Microsoft Business Intelligence Platform. You’ll explore key concepts like data warehouses and data models, setting the stage for the more advanced topics to come.
Module 2 dives into the core of SSAS by introducing you to multidimensional databases. Learn how to secure your data with cube security and get hands-on experience in creating and configuring an SSAS cube. This module also covers essential topics like data sources and data source views, rounding off with adding a dimension to a cube.
In Module 3, you’ll delve deeper into the world of SSAS cubes and dimensions. Learn about attribute hierarchies, relationships, and how to sort and group attributes effectively. The module also covers the concept of slowly changing dimensions, a crucial aspect of SQL SSAS.
Module 4 focuses on the quantitative aspects of SSAS, covering measures and measure groups. Learn how to define measures, set up measure groups, and understand their relationships. You’ll also explore different storage options for measure groups, all within the SQL SSAS environment.
Get acquainted with the Multidimensional Expressions (MDX) language in Module 5. This module covers MDX fundamentals and teaches you how to add calculations to an SSAS cube. You’ll also learn how to query a cube using MDX, a key skill in SSAS SQL.
Module 6 empowers you to customize your SSAS cubes to meet specific business needs. Learn how to set up Key Performance Indicators (KPIs), create actions, and add perspectives and translations to your cubes, enhancing their functionality and usability.
Module 7 introduces you to the SSAS tabular model, an alternative to the traditional multidimensional model. Learn how to create a tabular data model, configure relationships and attributes, and set up your data model for an enterprise BI solution.
Dive into the world of Data Analysis Expressions (DAX) in Module 8. This module covers DAX fundamentals and advanced topics like calculated columns, relationships, measures, and time intelligence. You’ll also explore KPIs and parent-child hierarchies within the SSAS tabular model.
Wrap up your SSAS training with Module 9, which provides an overview of data mining in SQL Server SSAS. Learn how to create custom data mining solutions, validate a data mining model, and consume the model for actionable insights.
By the end of this course, you’ll be well-equipped to leverage Microsoft SSAS for your business intelligence needs, from creating SSAS cubes to mastering the SSAS tabular model. Join us to become an SSAS expert today!
This course is ideal for a wide range of professionals and enthusiasts who are interested in diving into the world of data analytics and business intelligence. Here are some groups who would particularly benefit:
By the end of this course, participants will have a solid understanding of what SSAS is, how to create SSAS cubes, and how to use the SSAS tabular model for business intelligence and data analytics. This makes the course a valuable resource for anyone looking to upskill or break into the field of data analytics.
Below is a sample salary table that outlines the potential earnings for professionals who work with SQL Server Analysis Services (SSAS). Please note that these figures are approximate and can vary based on factors such as experience, location, and the specific responsibilities of the role. All figures are in U.S. dollars and are based on annual earnings.
Job Title | Entry-Level Salary | Mid-Level Salary | Senior-Level Salary |
---|---|---|---|
SSAS Developer | $70,000 – $85,000 | $86,000 – $105,000 | $106,000 – $130,000 |
Data Analyst (SSAS Specialization) | $60,000 – $75,000 | $76,000 – $95,000 | $96,000 – $115,000 |
Business Intelligence Analyst | $65,000 – $80,000 | $81,000 – $100,000 | $101,000 – $125,000 |
Database Administrator (DBA) | $72,000 – $88,000 | $89,000 – $110,000 | $111,000 – $140,000 |
BI Developer (SSAS Focus) | $75,000 – $90,000 | $91,000 – $110,000 | $111,000 – $135,000 |
Data Architect | $90,000 – $110,000 | $111,000 – $135,000 | $136,000 – $160,000 |
Data Engineer (SSAS Expertise) | $85,000 – $100,000 | $101,000 – $120,000 | $121,000 – $145,000 |
Data Scientist (SSAS Knowledge) | $95,000 – $115,000 | $116,000 – $140,000 | $141,000 – $170,000 |
IT Manager (SSAS Experience) | $100,000 – $120,000 | $121,000 – $145,000 | $146,000 – $175,000 |
Consultant (SSAS Specialization) | $80,000 – $100,000 | $101,000 – $125,000 | $126,000 – $155,000 |
Additional Benefits:
It’s important to note that these are general estimates and actual salaries can vary based on a multitude of factors including the company, the cost of living in the location, and additional skills that complement SSAS expertise.
Understanding the key terms related to Microsoft SQL Server 2019 Analysis Services (SSAS) is crucial for professionals working in fields like business intelligence, data analysis, and database management. This knowledge base will help in comprehending the functionalities, features, and technical aspects of SSAS, enabling better utilization of this powerful tool for data analytics and business intelligence.
Term | Definition |
---|---|
SSAS | SQL Server Analysis Services, a tool for online analytical processing and data mining in SQL Server. |
Business Intelligence (BI) | Technologies, applications, and practices for collecting, integrating, analyzing, and presenting business information. |
Data Modeling | The process of creating a data model for the data to be stored in a database. |
Multidimensional Database | A type of database that is optimized for data warehouse and online analytical processing (OLAP) applications. |
Cube | A data structure in SSAS that allows data to be modeled and analyzed in multiple dimensions. |
Dimension | A structure that categorizes data in order to enable users to answer business questions. |
MDX (Multidimensional Expressions) | A query language for OLAP databases, similar to SQL. |
Tabular Data Model | A type of data model in SSAS that uses relational modeling techniques. |
Data Mining | The practice of examining large databases to generate new information. |
SQL Server | A relational database management system developed by Microsoft. |
OLAP (Online Analytical Processing) | An approach to quickly answer multi-dimensional analytical queries. |
Data Warehouse | A system used for reporting and data analysis, storing historical data. |
ETL (Extract, Transform, Load) | A process in database usage and data warehousing. |
DAX (Data Analysis Expressions) | A collection of functions, operators, and constants for data analysis and calculations. |
Query | A request for data or information from a database. |
Relational Database | A database structured to recognize relations among stored items of information. |
Data Mart | A subset of a data warehouse focused on a specific business line or team. |
Star Schema | A type of database schema that is common in data warehousing. |
Snowflake Schema | A more complex version of a star schema used in a data warehouse. |
KPI (Key Performance Indicator) | A measurable value that demonstrates how effectively a company is achieving key business objectives. |
Partitioning | The process of dividing a database into multiple pieces for performance or organization reasons. |
Aggregation | A process in OLAP where summary data is computed and stored. |
Hierarchies | Structures in data that represent relationships where one item contains other items. |
Measure | A property on which calculations (like sum, count, average) can be made in a cube. |
Data Source | The location and method of connection to the data that is to be processed and analyzed. |
Analysis Services Scripting Language (ASSL) | The XML language used to define objects in SSAS. |
SQL Server Management Studio (SSMS) | An integrated environment for managing SQL Server infrastructure. |
Power BI | A business analytics service by Microsoft for interactive visualizations and business intelligence capabilities. |
Data Cleansing | The process of detecting and correcting (or removing) corrupt or inaccurate records from a database. |
Dimensional Modeling | A data modeling technique in data warehouse design, optimized for querying and reporting. |
Fact Table | A table in a star schema of a data warehouse that contains measured quantities. |
SSAS stands for SQL Server Analysis Services, a part of Microsoft’s SQL Server suite. It is used for advanced analytics and business intelligence. SSAS allows you to create multidimensional cubes and tabular data models, enabling fast and efficient querying and analysis of large datasets. It plays a crucial role in helping businesses make data-driven decisions by providing tools for data modeling, analysis, and visualization.
SSAS offers two types of data models: Multidimensional and Tabular. The Multidimensional model is more traditional and is optimized for complex queries and OLAP (Online Analytical Processing). The Tabular model is newer and is generally easier to set up, offering faster performance for simpler queries. Both have their own use-cases, and the choice between the two often depends on the specific needs of a project.
ISSAS integrates seamlessly with other Microsoft tools like SQL Server, Excel, and Power BI. You can import data from SQL Server into SSAS to create cubes or tabular models. These can then be easily accessed and visualized using Excel or Power BI, providing a comprehensive business intelligence solution within the Microsoft ecosystem.
MDX, or Multidimensional Expressions, is a query language for OLAP databases like SSAS. While it’s not strictly necessary to know MDX to work with SSAS, having a good grasp of MDX can be beneficial, especially if you’re working with the Multidimensional model. It allows you to write custom queries and calculations, providing more flexibility in your data analysis.
Professionals with expertise in SSAS have a range of career opportunities available to them, including roles like SSAS Developer, Business Intelligence Analyst, Data Architect, and Database Administrator. These roles are in high demand across various industries, and proficiency in SSAS is often associated with higher earning potential.
Course Outline
James is a full-stack developer with over 40 years of experience. He has developed applications across all major industries and for Fortune 100 companies as well as local small businesses. James has also been teaching technology courses for over 20 years. In addition to his extensive background in technology, he has also worked as a professional opera singer.
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