In this article, I’m going to explain what many-to-many relationships in SSAS are and how to implement the same in a SQL Server Analysis Services (SSAS) project. For the sake of this article, we will only consider the Multidimensional Cube and not the Tabular. Also, this article assumes that you have some fair knowledge of building SSAS cubes from scratch.
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In this article, we look at how to create different types of relationships between two or more tables in the Power BI data model.
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Deleting or changing objects may affect other database objects like views or procedures that depends on them and in certain instances, can “break” the depending object. An example can be that if a View queries a table and the name of that table changes. The View will no longer function.
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In relational database systems, objects have different types of relationships with each other. Apart from table relationships (such as one to one, one to many and many to many), objects such as stored procedures, views, custom functions also have dependencies on other objects. It is important to understand object dependencies, particularly if you want to update an object that depends upon other objects.
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Just like in Santa’s Bag of Goodies, every release of SQL Server often has something for everyone – be it enhancements to DMVs for the DBAs, new functions for T-SQL developers or new SSIS control tasks for ETL developers. Likewise, the ability to effectively support many-to-many relationships type in SQL Graph has ensured that there is indeed something in it for the data warehouse developers in SQL Server 2017. In this article, we take you through the challenges of modelling many-to-many relationships in relational data warehouse environments and later demonstrate how data warehouse teams can take advantage of the many-to-many relationship feature in SQL Server 2017 Graph Database to effectively model and support their data warehouse solutions.
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A few years ago, one common business case I came across in my professional career that required modelling of data into a many-to-many entity relationship type was the representation of a consultants and their projects. Such a business case became a many-to-many entity relationship type because whilst each project can be undertaken by several consultants, consultants can in turn be involved in many different projects. When it came to storing such data in a relational database engine, it meant that we had to make use of bridging tables and also make use of several self-joins to successfully query the data.
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The graph database is a critically important new technology for data professionals. As a database technologist always keen to know and understand the latest innovations happening around the cutting edge or next-generation technologies, and after working with traditional relational database systems and NoSQL databases, I feel that the graph database has a significant role to play in the growth of an organization. Not only are traditional database systems generally inefficient in displaying complex hierarchical data, but even NoSQL lags a little. We usually see a degradation in performance with the number of levels of relationship and database size. Also, depending on the relationship, the number of joins may increase as well.
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The Multidimensional Cube option of Analysis Services has handled many-to-many relationships with ease for many versions before 2016. The Tabular had a work around using DAX formulas until the release of SQL Server 2016. There are still some limitations to many-to many in Tabular but of course, there are some “tricks” to overcome the limitations. But, the many-to-many relationship will be in businesses data for many years to come. A solution has to be provided when it comes to Analysis Service databases.
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Sometimes, we need to drop a SQL Server Login because we gave an access to a person who left, this login was used by an application that has been decommissioned for example.
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We have already discussed quite some design tips for building Analysis Services (SSAS) Multidimensional cubes and dimensions:
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In Part 1 of this article, we built a case for the benefits of documenting our schema via the use of a stored procedure, as well as the ways this information can be used for DML operations on a relational database. Below, we will continue where we left off by speeding up our SQL queries and then putting everything together so that we can demo a usable script that will hopefully make your life easier!
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In a typical OLTP environment, we want to maintain an acceptable level of data integrity. The easiest way to do this is through the use of foreign keys, which ensure that the values for a given column will always match that of a primary key in another table.
Over time, as the number of tables, columns, and foreign keys increase, the structure of that database can become unwieldy. A single table could easily link to thirty others, a table could have a parent-child relationship with itself, or a circular relationship could occur between a set of many tables.
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