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Azure SQL Database is built on the foundation of the SQL server database and therefore, kept in sync with the latest version [2] of it by using the common code base. Since the cloud version of the database technology strives to decouple it from the underlying computing infrastructure, it doesn't support some of the context specific T-SQL ...
In Azure Data Explorer, unlike a typical relational database management systems (RDBMS), there are no constraints like key uniqueness, primary and foreign key. [26] The necessary relationships are established at the query time. [27] The data in Azure Data Explorer generally follows this pattern: [28] Creating Database, Ingesting data, Query the ...
Azure Cache for Redis is a managed implementation of Redis. StorSimple manages storage tasks between on-premises devices and cloud storage. [26] Azure SQL Database works to create, scale, and extend applications into the cloud using Microsoft SQL Server technology. It also integrates with Active Directory, Microsoft System Center, and Hadoop.
Typically multiple examinations were required to obtain either a hardware centered certification such as the MCSE (Microsoft Certified Systems Engineer) or a more software orientated offering such as the MCSD (Microsoft Certified Solutions Developer). The process of earning certification has changed multiple times since its initial inception.
Cloud Insights is an SaaS application for monitoring infrastructure application stack for customers consuming cloud resources and also build for the dynamic nature of microservices and web-scale infrastructures. Cloud Insights uses similar to OnCommand Insight front-end API but different technology on the back-end.
Software analytics is the analytics specific to the domain of software systems taking into account source code, static and dynamic characteristics (e.g., software metrics) as well as related processes of their development and evolution.
An application analyst (also applications systems analyst) is someone whose job is to support a given application or applications. This may entail some computer programming, some system administration skills, and the ability to analyze a given problem, diagnose it and find its root cause, and then either solve it or pass the problem on to the relevant people if it does not lie within the ...
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...