Completing SQL. Part 3: Complementing SQL. The life of Visual Studio extensions. Working with IO. Using SQL in unconventional ways.

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What is this article about?

This is the third article in the series of articles on the life of IDE developers for databases.

Its structure will be similar to the first one and the second one, even so, I am not going to tell you about the parsing of text. This article will present information on some tricks working with files and various problems of creating a big desktop application on the .NET platform.

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Completing SQL. Part 2: Optimizing string processing and file opening

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What is this article about?

This is the second piece from the series of articles about the life of database IDE developers. Its structure is similar to that of the previous article. Just like in the first one, I’ll talk about the issues we faced and the solutions we arrived at – both effective and not so much. To understand this article, you don’t have to read the first part in its entirety, but the first few paragraphs would be useful to help you grasp the context.

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Best SQL Tools for Database Developers and Administrators

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For a SQL database developer, it is essential to have the necessary SQL tools in hand, which enable to reduce the development time of given functionality. The same is true for the tester. For a database administrator, it is also important to have monitoring tools.

In this article, we are going to look at some MS SQL Server development and monitoring tools, such as:

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Basics of Working with JSON in SQL Server

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JSON – A Brief Background

JSON is an acronym for JavaScript Object Notation, that became popular a little over seventeen years ago. JSON is essentially a data format, it was popularized by Douglas Crockford, a well-known programmer with an interesting history who was also involved in the development of JavaScript. JSON has nearly replaced XML as a cross-platform data exchange format. It is reported to be lightweight and easier to manipulate compared to XML. In AWS CloudFormation, templates, which are actually JSON (or YAML) formatted documents, are used to describe AWS resources when automating deployments.

JSON is also used extensively in NoSQL databases such as the increasingly popular MongoDB. Virtually all the Social Media giants expose APIs that are based on JSON. I am sure you begin to get the idea of how widespread its applications have become. JSON was standardized in 2013 and the latest version of the standard (ECMA-404: The JSON Data Interchange Syntax) was released in 2017.

SQL Server introduced support for JSON in SQL Server 2016.

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Collation in SQL Server

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Introduction

You must have already heard the term “Collation” in SQL Server. Collation is a configuration that determines how character data sorting is done. This is an important setting that has a huge impact on how the SQL Server database engine behaves in dealing with character data. In this article, we aim to discuss collations in general and show a few examples of dealing with collations.

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Applying SQL Transformations and Handling Missing Values in Azure ML

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In this article, we will introduce SQL transformations in action. We will also see how to handle missing values in our dataset.

Consider a scenario of a movie rating dataset containing records of different movies along with the average user ratings associated with each movie. The ratings are in numeric form ranging from 1 to 10 with 1 as the lowest rating and, respectively, 10 as the highest rating (though no movie in the history has achieved 10 rating:). Suppose that we want to convert the numeric ratings into categorical ratings. For instance, we want to replace ratings of 1-3 with the categorical value “poor”, of 4-6 with “average” while ratings of 7-10 will have the value “good”. We can accomplish it with SQL transformation in Azure ML Studio. Read More