In my previous article, I have explained the process of installing and creating a failover cluster and how to enable the AlwaysOn availability group.
In this article, I am going to explain the step-by-step deployment process of SQL Server AlwaysOn Availability groups using wizards. The deployment configurations look the following way:
In my previous article, I have explained the architecture of the AlwaysOn availability group and pre-requisites to deploy it successfully. In this article, I will explain the process of installing pre-requisites to deploy the SQL Server AlwaysOn availability group.
For the demonstration, I have prepared a demo set up at my work station. See the following components:
| Virtual Machine | Host Name | Purpose |
| Domain Controller | DC.Local | The domain controller is installed on this machine |
| Primary Replica | SQL01.DC.Local | This machine acts as a Primary replica in the Availability group |
| Secondary Replica | SQL02.DC.Local | This machine acts as a Secondary replica in the Availability group. This replica is in a Synchronous commit mode |
| Secondary Replica with | SQL03.DC.Local | This machine acts as a secondary replica in the Availability group. This replica is in an Asynchronous commit mode |
I will explain the following actions:
With all the advancements in SQL Server and data, it feels great that such native SQL tools as Alerts and Operators are still available. They are the key SQL Server Agent features that are related to anything in SQL Server automation.
Their roles are clear:
This article tells about advanced methods of Full-Text Search usage for data professionals. It enables better search experience in their day-to-day database analysis.
Additionally, the readers will get beyond basic knowledge of Full-Text Search and its implementation. You will see the importance of advanced searching methodology using Full-Text Search to get quick and reliable results.
First, let’s go through prerequisites since the article is focused on advanced uses of Full-Text Search.
The article assumes that to implement the walk-through(s), the readers are familiar with writing and running T-SQL scripts against databases with a basic understanding of SQL Server. Read More
This article talks about the full-text search and its implementation for the beginners to get started.
Additionally, the readers will know the important things to make the full-text search work properly.
This article also highlights the importance of using Full-Text Search to match text patterns and meet similar sorts of requirements.
Let’s understand the Full-Text Search with the help of definitions.
Full-Text Search is used to optimally search character-based data for quick answers.
Full-Text Search in SQL Server and Azure SQL Database (cloud version of SQL database) let users and applications run full-text queries against character-based data in SQL Server tables. Read More
Azure data studio is a cross-platform database management tool. This tool is used to connect, configure, and administrate SQL Server instances installed locally or in the cloud. This tool can be installed on the following operating systems:
Initially, Microsoft introduced this tool as the “SQL Server operations studio.” Later they renamed it Azure Data Studio. You can get more information about it here.
In this article, I am going to explain the step-by-step installation process of SQL Server 2017 and Azure data studio on Ubuntu. I have divided the article into two parts. Firstly, we will install SQL Server 2017, and later I will explain the process of installing Azure Data Studio. For demonstration purposes, I have created a virtual machine and installed Ubuntu 18.04 on it. You can download Ubuntu here. Read More
This article gives a high-level understanding of dynamic data masking in SQL Server along with its use cases, best practices and security implications for advanced SQL users (including developers and testers). Additionally, the readers of this article are going to get familiar with applying different types of dynamic data masking. The article also highlights the importance of advanced-level use of data masking in day-to-day database development and testing tasks.
Containerization has become the dominant topic after the Server Virtualization and Cloud Computing Eras. While many thought leaders assert that current computing paradigms are not particularly new, containerization, as we have it today, can be viewed as having been born after cloud computing. Using containers on-premise allows us to perform migration to the cloud while building a containerized environment mimics the features of solutions deployed in the cloud.
Recently, I was assigned to a project – our customer purchased a new SAN, and they wanted us to replace the old one with a new SAN. User and system databases utilized SAN, meanwhile, the operating system and SQL were installed on the server’s local disks. Lucky us..!! The idea was to move the existing user and system databases to a new SAN and keep the downtime as minimum as possible. So, to minimize the downtime, instead of reinstalling OS and SQL, we decided to use the following approach.
In most scenarios when working with data, users do not require all the data in each instance of a query. T-SQL (and SQL of course) provide means of retrieving only part of a data set in a table or a similar structure. The WHERE, HAVING, TOP and OFFSET-FETCH clauses are used to accomplish this in different ways. Read More
