Without a doubt, few technologies in SQL Server cause as much confusion and the spread of misinformation as indexes. This article looks at some of the most asked questions and a few that should be asked but often are not. We’ll be using SQL Server 2016 for the examples and a tool, for SQL Server query execution plan analysis, ApexSQL Plan, to explore the effects of indexes on a typical business problem: A table of customers.
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As a DBA, I am often asked to automate tasks. In addition to this, I generally work with a lot of developers that need to test with data refreshing on a regular basis. A lot of the time, I am asked to either stand up a new test environment or overwrite/refresh an existing one. Now, these tasks are not difficult to complete, but why not just automate them into a SQL Agent job and just run it whenever you need it or schedule it and let the job do all the work? If you have read some of my previous articles (What is causing database slowdowns or Simple SQL Server automated user auditing), you will know I am a big proponent of automating repeatable tasks. The best part of this is if you have multiple clients like I do is that you can script out the job, change the variables and use it over and over again. I cannot tell you how many times I say “I have a job for that” when a client asks me to automate a task.
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When you define SQL Server database tables, local variables, expressions or parameters, you should specify what kind of data will be stored in those objects, such as text data, numbers, money or dates. This attribute is called the SQL Server Data Type. SQL Server provides us with a big library of system data types that define all types of data that can be used with SQL Server, from which we can choose the SQL Server data type that is suitable for the data we will store in that object. You can also define your own customized user defined data type using T-SQL script. SQL Server data types can be categorized into seven main categories:
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In an era of remote storage and retrieval of data, including the cloud, data security plays a vital role, especially since it’s vulnerable during the transit. Situations like database backup or copy from or to the cloud, there is always a risk of data exposure to outside world lurking around one corner or the other. We have seen a noticeable surge in the technologies around protection and security of data from the world full of unsafe hands. Efforts are being made to protect data at a very granular level of the encryption hierarchy. Protection of business data cannot be stressed upon more.
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SQL unit testing is rising in popularity amongst database developers. The importance of Code Quality in Software Development has increased over the period of time. Everyone wants to follow coding standards and write clean code. However, to timely deliver a product to Business we end up in violating design principles, writing fewer unit tests, increasing code complexity and breaching best practices. This increases the overall Technical Debt of the application and has the potential to halt future development work by creating unplanned work.
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In SQL Server Management Studio 2016, Microsoft introduced a visual setting option to choose between the Blue and Light color theme. In SSMS 2016, SSMS 2017 and the latest SSMS 18, the user can switch between the Blue or Light theme by going to Tools | Options | Environment | General | Color theme:
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Linked servers allow to access data from another SQL Server or another data source (e.g. Excel) by using SQL Server Management Studio (SSMS) or Transact-SQL.
This article will explain how to create and configure a linked server to retrieve data from an Azure SQL database. Also, we will explain how to solve some common problems/issues during the process of creating a linked server to an Azure SQL database.
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If you’ve been developing in SQL Server for any length of time, you’ve no doubt hit this scenario: You have an existing, working query that produces results your customers or business owners say are correct. Now, you’re asked to change something, or perhaps you find out your existing code will have to work with new source data or maybe there’s a performance problem and you need to tune the query. Whatever the case, you want to be sure that whatever changes have been made (whether in your code or somewhere else), the changes in the output are as expected. In other words, you need to be sure that anything that was supposed to change, did, and that anything else remains the same. So, how can you easily do that in SQL Server?
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In SQL Server Management Studio (SSMS), it is possible to connect to the Azure Storage. The Azure Storage Account is useful because it creates replicas automatically in the cloud. You only need to upload your file to the Azure Storage Account and the replication is automatic. You can have multiple replications in different regions and Datacenters with few clicks.
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This articles gives you a performance comparison for NOT IN, SQL Not Exists, SQL LEFT JOIN and SQL EXCEPT.
In a previous article entitled Hands on Full-Text Search in SQL Server, we had an overview on the Full-Text feature as it’s implemented in SQL Server. We saw how to create Full-Text indexes and that they were stored inside a container called a Full-Text catalog. We’ve also seen that, by design, this kind of index will generate a fragmentation.
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Database migration is a vital task in any environment, complex or otherwise. Seamless migrations are the goal but the efforts required to ensure it are tremendous.
Backing up and restoring the database is surely a preferred and a robust approach, but does it work well in all situations? How do we plan this when the source and the destination databases need different configurations? How do we make such a migration seamless?
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SQL Server Maintenance Plans is a SQL Server Management Studio built-in feature that helps in creating a workflow of variant database administration tasks, which can be run automatically using a predefined schedule or manually triggered by the user.
SQL Server Maintenance Plans allow you to use typical database maintenance tasks or customize your own task using a T-SQL script that runs on the local server or group of SQL Servers, providing more flexibility to the database administration tasks.
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A big part of the technical debt in my organization’s data warehouse (DW) and business intelligence (BI) environments relates to hardcoded lookup data. This is data required by the business to make sense of transactional data but was never planned for in the underlying source system and consequently get injected into DW and BI solutions. Inevitably, it is only a matter of time before DW and BI team lose track of the places wherein the hardcoded data reside thus making it difficult to maintain. Furthermore, due to lack of documentation or staff retention, anyone who subsequently takes over these DW/BI solutions can unknowingly create duplicate lookup data. In this article, I explain how we reduced such technical debt in my organization by moving most of the hardcoded lookups into SQL Server Master Data Services (MDS).
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It is a common misconception that you need real production data, or production like data, to effectively tune queries in SQL Server. I am going to explain how you can compile the same execution plans as what your production environment would compile, so that you can tune them in a non-production environment, gaining these benefits.
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Today’s businesses work with huge volumes of data. Sometimes, understanding the granularity of the data is helpful in data administration. Understanding the granularity poses unique challenges though and the database administrator needs to balance the key business and technical metrics of the environment. One of the key technical metrics is disk space estimation, which is vital to capacity planning and forecasting. The simplest way to get this information is by using the system stored procedure, called, sp_spaceused. The growth metrics can be captured periodically and stored in a central repository, a repository dedicated to helping with capacity planning and forecasting the disk requirements. This is a critical part of the day-to-day activities of a database administrator.
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Availability groups can no longer be considered new. The feature was released in SQL Server 2012 but I often notice that SQL Server Failover Cluster Instances (FCIs) are much better understood. For a very long time, when people thought about SQL Server they thought about the entire instance as a unit. Microsoft has been implementing new features and services which begin to segment the concept of an instance from the concept of a database.
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The Multidimensional Cube option of Analysis Services has handled many-to-many relationships with ease for many versions before 2016. The Tabular had a work around using DAX formulas until the release of SQL Server 2016. There are still some limitations to many-to many in Tabular but of course, there are some “tricks” to overcome the limitations. But, the many-to-many relationship will be in businesses data for many years to come. A solution has to be provided when it comes to Analysis Service databases.
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Let’s say we have a database role that we don’t need anymore because we defined another security policy using another database role. What are the steps to follow in order to properly drop this database role? That’s the question this article will try to answer, covering as many cases as possible.
In the following article, we will consider the simple steps we can follow in order to do this task using both SSMS and T-SQL. Then, we will focus on some facts that will lead us to the conclusion that, if we do it this way, it won’t work every time. We will list some situations where it could fail and define a test case situation in order to create a stored procedure that will do the job correctly, in all cases bymanaging these situations.
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Before going through the main concern of this article, indexing the foreign key columns, let’s take a small trip back to review the SQL Server Indexes and Foreign Key concepts.
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Azure Cosmos DB is a low-latency, high throughput, globally distributed, a multi-model database which can scale within minutes and offers 5 consistency options to let you decide how to deal with the CAP theorem.
Azure Cosmos DB used to be known as Document DB, but since additional features were added it has now morphed into Azure Cosmos DB. The name was chosen to spark the innovation and imagination of developers around the globe.
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In SQL Server, it`s not always required to fully understand the internal structure, especially for performance and optimization, if the database design is good, because SQL Server is a very powerful Relational Database and, as such, it has many inbuilt optimization processes which assure a response to the users as fast as possible. But it is always beneficial for the SQL Server developers and administrators to understand the internal structure of the SQL Server so that they can understand and fix the problems that slowed the response of the Database.
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SQL Server Management Studio is an integrated graphical interface that is used to configure, manage, monitor and administrate the SQL Server instances hosted on the local machine, on a remote server or in the cloud. It provides us with editing, debugging and deploying environment for the T-SQL, XML, MDX and DMX languages.
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