In this article, we will discuss how different types of indexes in SQL Server memory-optimized tables affect performance. We will examine examples of how different index types can affect the performance of memory-optimized tables.
SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
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This is part 3 of “MS SQL Server- Zero to Hero” and in this article, we will be discussing about the SCHEMAS in SQL SERVER. Before getting into this article, please consider to visit previous articles in this series from below,
In part one, we learned the basics of data, database, database management system, and types of DBMS and SQL.
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Since the release of SQL Server 2017 for Linux, Microsoft has pretty much changed the entire game. It enabled a whole new world of possibilities for their famous relational database, offering what was only available in the Windows space until then.
I know that a purist DBA would tell me right away that the out of the box SQL Server 2019 Linux version has several differences, in terms of features, in regards to its Windows counterpart, such as:
However, I got curious enough to think “what if they can be compared, at least to some extent, against things that both can do?” So, I pulled the trigger on a couple of VMs, prepared some simple tests, and collected data to present to you. Let’s see how things turn out!
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Would SQL Server graph database features fit your next project? While you may not know the definitive answer right now, you might be wondering, “What problems does it solve?”.
In broad terms, this post tackles the issues of what a graph database is, what its uses are, and what benefits you and your stakeholders can derive from SQL Server graph database capabilities. And see for yourself why that is not another excuse for using something new for your new project. So, if you haven’t yet checked out this “feature”, it’s time to take a look at how cool this is.
Who knows? This can be a great solution for your next project.
So, let’s dive right in.
While we know rows, columns, primary and foreign keys are part of relational databases, graph databases use nodes and edges. They are mainly suitable for many-to-many relationships. Unlike the HierarchyID, a node can have more than 1 parent, while HierarchyIDs are limited to one-to-many relationships only.
Meanwhile, nodes can have properties, and edges define the relationship between nodes.
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