SQL Server Always ON Availability Group is a solution meant to achieve high availability and disaster recovery for SQL Server databases. We can configure this functionality between the Windows-based SQL Server installations, Linux-based SQL Server installations, and even between Linux and Windows-based SQL Server installations together.
Typical queries in the SELECT * FROM table format are sometimes not enough. When the data for a query is not in one table, but in several, or when it is necessary to specify several selection parameters at once, you will need more sophisticated queries.
This article will explain how to build such queries and provide examples of complex SQL queries.
When we think about querying databases, the first thing that pops to mind is usually some SQL query. Then other questions arise in regards to the database type, connection, query design, etc.
LINQ to Entities combined with Entity Framework allows the developers to skip a big portion of these questions and worries.
Entity Framework handles the database structure and connection while LINQ to Entities gives the possibility to write database queries in the same way as for any other data collection operation with LINQ.
In the previous articles, we covered sequential steps necessary to build a virtual machine, install Ubuntu 18.04 operating system, and configure SQL Server 2019 on that Ubuntu machine. This is 3rd part of this series. Our goal is to demonstrate the step-by-step process of installing SQL Server Tools and its components on the Ubuntu 18.04 Linux system.
The main idea around the SQL Server function called STUFF is concatenating multiple columns into a single column with more flexibility than the CONCAT function would provide. Besides, STUFF can be combined with other techniques for some interesting effects.
In this article, we’ll explore the possibilities that the STUFF command provides for SQL Database specialists.
Recently we encountered an interesting performance problem on one of our SQL Server databases that process transactions at a serious rate. The transaction table used to capture these transactions became a hot table. As a result, the problem showed up in the application layer. It was an intermittent timeout of the session seeking to post transactions.
This happened because a session would typically “hold on” to the table and cause a series of spurious locks in the database.
Customer Segmentation Analysis is a popular business issue, especially in Retail domain, where companies need to discover the data and group customers based on customer attributes, such demographics and customer transactions. The results of customer segmentation analysis let the retailers understand the customers’ behavior and create effective campaigns.
This tutorial will describe the usage of Data Flow in Oracle Analytics for resolving the Customer Segmentation problem (the data is hosted in Autonomous Data Warehouse).
Analytic functions are special kinds of pre-built functions that come with PostgreSQL by default. They allow you to execute a variety of analytical workloads on your datasets and prepare results.
In the world of cloud computing, where we need to provide insights to customers in a swift and meaningful way, understanding these analytical functions solves a lot of challenges.
This article will talk about the most common analytical functions used in PostgreSQL.
Hash indexes are an integral part of databases. If you’ve ever used a database, chances are that you have seen them in action without even realizing it.
Hash indexes differ in work from other types of indexes because they store values rather than pointers to records located on a disk. This ensures faster searching and insertion into the index. That’s why hash indexes are often used as primary keys or unique identifiers.
Imagine you have a business-critical MS SQL Server serving requests for both OLTP and some batch processing tasks, as data gets pulled by another team. That might be reporting services or extracting data to push to another data warehousing environment, or else. Under this scenario, your SQL Server is treating numerous requests from multiple applications. This in turn affects overall performance and can impact higher priority tasks.
