Structured data is data that adheres to a pre-defined data model and conforms to a tabular Relational Database Management System (RDBMS). SQL Server is known as an RDBMS server, and as such when we design our database and our queries, we treat the data as values stored in a table structure of columns and rows.

In fact, the boundaries of what we can do directly using SQL Server are beyond the relative database model query. These boundaries are gradually blurring from the early days of SQL Server to the latest version of SQL Server 2022, with new types of data and new features being added to new versions.

In this session, we will review how SQL Server supports Unstructured Data (different data types and features), and we will get into an in-depth discussion on the question what makes SQL Server a Tabular Database behind the scenes (Is SQL Server really stores tables on the disk?) and what is the difference between managing data in Tabular model to managing same data in NoSQL models.

Once we break the psychological boundaries between tabular and unstructured data, we will be able to think outside the box and take our daily work to the next level. In the second part of the session, I will present tricks you didn't think of to improve performance dramatically using unstructured data types to solve needs related to simple structured data types. For example, did you thought using Documents data types to avoid loops, or using Geometry Data type to solve dates ranges issues or "Gaps and Islands" issues, and what about designing the optimal solution for hierarchical data?

This session is for 90+ minuts