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via Udemy |
Go to Course: https://www.udemy.com/course/ssas2019/
Certainly! Here’s a comprehensive review and recommendation for the Coursera course on SQL Server Analysis Services (SSAS): --- **Course Review and Recommendation: Mastering SQL Server Analysis Services (SSAS) on Coursera** **Overview:** This Coursera course offers an in-depth exploration of SQL Server Analysis Services (SSAS), a powerful multi-dimensional OLAP server and analytics engine. Designed for aspiring data analysts, BI developers, and database administrators, this course covers essential concepts, including cube design, data modeling, MDX scripting, and administrative tasks necessary to leverage SSAS for high-performance data analysis. **Content Highlights:** - Detailed understanding of SSAS architecture, including multidimensional and tabular models. - Step-by-step guidance on creating and managing cubes, dimensions, and measures. - Extensive coverage of data source connectivity, data source views, and named queries. - Practical insights into schema design, such as star and snowflake schemas, optimized for analytical queries. - Hands-on experience with MDX language for complex data analysis. - Roles and responsibilities of SSAS developers and administrators, including installation, configuration, security, and maintenance. - Demonstrations of client tools like Excel’s PivotTable, SSRS, Cognos, and ProClarity for data visualization and reporting. **Strengths:** - The course provides a solid theoretical foundation and practical exercises, making complex topics accessible even for beginners. - Emphasis on real-world applications, such as navigating cube data with various client tools. - Clear explanations of critical components like dimension hierarchies, fact tables, measures, and schema design. - The inclusion of detailed roles for developers and admins, giving a comprehensive understanding of the SSAS ecosystem. **Who Should Enroll:** - Data professionals seeking to enhance their BI and OLAP skills. - Database administrators tasked with managing SSAS environments. - Business analysts looking to create high-performance analytical reports. - Anyone interested in mastering multidimensional data modeling and analysis. **Final Thoughts & Recommendation:** This course is highly recommended for anyone interested in advancing their skills in multidimensional analysis and OLAP technologies. The curriculum is well-structured, blending theory with practical application, which is crucial for mastering SSAS. Upon completion, you'll be equipped to design effective cubes, perform sophisticated data analysis, and manage SSAS environments proficiently. Whether you're aiming to improve your analytics capabilities or seeking a career in BI development, this course on Coursera is a valuable investment. It offers the foundational knowledge and hands-on experience needed to thrive in the field of data analysis and business intelligence. **Enroll today to unlock the full potential of your data using SSAS!** --- Would you like a shorter summary or additional details about specific modules?
SSAS full form is SQL Server Analysis Services.Multi-dimensional OLAP server as well as an analytics engine that allows you to slice and dice large volumes of data.SSAS is an analysis service platform, which can be used to create and manage the analysis database.SSAS Contains Preaggregated data , internally creates analysis database, and once the analysis database is ready, it can be used for many purposesIt has 2 variants Multidimensional and Tabular.One or more cubes can be presented in the analysis database.Advantages of SSAS:· High performance reports· Multidimensional data analysis· Slice and data analysis· Data mining purposeUsing the Cube Data Base or Analysis Data Base:There are several client tools to use Analysis database.a. Analyzing the cube database data in the SSDT/BIDS browser.b. Using the PIVOT table in the excel application to connect and work with cube database.c. Using Reporting tools (SSRS, Cognos) to generate the reports.d. By writing the MDX queries in the cube database.e. Using Panorama Novaview and ProClarity tools to analyze the data.SSAS Developer Roles:· Understanding the Data base structure· Designing the cubes· Scripting· Mdx languageSSAS Admin Roles:· Installation· Configuration· Deployment· Processing· Security· Managing[Backup and Restore]· Monitoring & Trouble shootingThe basic concepts of OLAP include:CubeDimension tableDimensionLevelFact tableMeasureSchemaData Source:Connection string that defines how Analysis Services connects to a physical data store.Data Source ViewsA data source view contains the logical model of the schema used by Analysis Services database objects-namely cubes, dimensions, and mining structures. A data source view is the metadata definition, stored in an XML format.Named Queries:A Named Query is a SQL expression represented as a table. It Can be used to divide large and complex dimension table to smaller and simple dimensions, it can also help us to unite columns from multiple tables to single table.It allows us to extend our table schema without modifying underlying base tables.CubeThe basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that's been aggregated to allow queries to return data quickly.Cubes are ordered into dimensions and measures. Dimensions come from dimension tables, while measures come from fact tables.Dimension tableA dimension table contains hierarchical data by which you'd like to summarize. Examples would be an Orders table, which you might group by year, month, week, and day of receipt, or a Books table that you might want to group by genre and title.DimensionEach cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data: time or category in the examples above. Typically, a dimension has a natural hierarchy so that lower results can be "rolled up" into higher results. For example, in a geographical level you might have city totals aggregated into state totals, or state totals into country totals.LevelEach type of summary that can be retrieved from a single dimension is called a level. For example, you can speak of a week level or a month level in a time dimension.Fact tableA fact table contains the basic information that you wish to summarize. This might be order detail information, payroll records, drug effectiveness information, or anything else that's amenable to summing and averaging. Any table that you've used with a Sum or Avg function in a totals query is a good bet to be a fact table.MeasureEvery cube will contain one or more measures, each based on a column in a fact table that you'd like to analyze. In the cube of book order information, for example, the measures would be things such as unit sales and profit.SchemaFact tables and dimension tables are related, which is hardly surprising, given that you use the dimension tables to group information from the fact table. The relations within a cube form a schema. There are two basic OLAP schemas: star and snowflake.Star Schema:Every dimension table is related directly to the fact table.Snowflake Schema:Some dimension tables are related indirectly to the fact table.For example, if your cube includes OrderDetails as a fact table, with Customers and Orders as dimension tables, and Customers is related to Orders, which in turn is related to OrderDetails, then you're dealing with a snowflake schema.