Before jumping into creating a cube or tabular model in Analysis Service, the database used as source data should be well structured using best practices for data modeling. Some might say use Dimensional Modeling or Inmon’s data warehouse concepts while others say go with the future, Data Vault. No matter what conceptual path is taken, the tables can be well structured with the proper data types, sizes and constraints.
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As a Microsoft SQL Server Database Consultant, I have to visit multiple companies with variety of SQL Server versions and patch level. Generally speaking, we need to analyze the environment of these clients and get information about the infrastructure. To do so, we have to run multiple scripts and tools on almost all the machines at client. Though it’s a part of our job but at times it takes hours to get even the basic information about the SQL Server versions, patch level, configuration and small details which even client does not have.
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Many DBA’s and database developers aren’t very familiar with DBCC commands (aka Database Console Commands) because they don’t always have the chance to work with them. In this article, we’ll provide a simple primer on DBCC commands with specific examples
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Context
As SQL Server database administrators, we should all know that, most of the time, a database user is linked to a SQL Server login. We do this to tell SQL Server that a SQL login LoginA has access to database Db1 using the context and permissions of UserA database user.
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Microsoft released many new features in Reporting Services with the SQL 2016 release.:
In the articles How to partition an SSAS Cube in Analysis Services Multidimensional and Benefits of Partitioning an SSAS Multidimensional Cube, the concept of measure group partitioning is introduced and the advantages are clearly illustrated. One of the biggest advantages of partitioning is partition elimination, where only the partitions necessary to satisfy the query are read instead of all the data.
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People love taking shortcuts because it’s the easiest way to do things faster; computers are no exception. Shortcuts, particularly ones performed by keyboard, can save you hours of time once applied properly. Learning keyboard shortcut saves you a lot of time; you’ll definitely notice a boost the productivity because you’re not unnecessarily reaching for a mouse all the time.
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There is a common need in reporting to aggregate or return data that is crunched based on date attributes. These may include weekdays, holidays, quarters, or time of year. While any of this information can be calculated on the fly, a calendar table can save time, improve performance, and increase the consistency of data returned by our important reporting processes. In my previous article, you could learn about designing of a calendar table.
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Whilst discussing the evolution of SQL Server Data Tools in my previous article, I touched on a new feature, Package Parts, which is available in SSDT 2015. This article takes an in-depth analysis of the Package Parts feature, particularly, as it relates to Data Warehouse development.
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The SQL Server Always On Availability Groups concept was introduced the first time in SQL Server 2012 as an enterprise-level high availability and disaster recovery solution that will replace the database mirroring feature. Always On Availability Group provides a high availability solution on the group level, where each group can contain any number of databases that can be replicated to multiple secondary servers known as Replicas.
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This article discusses a simple solution of how to monitor SQL service availability across multiple servers and reporting. To build this I’ll use SQL Server with simple PowerShell script and cmdlets which generate JSON data and displays results in HTML
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The title of this post should have been “How to implement wildcard search functionality with Always Encrypted, make deterministic encryption safer, and load initial data using SqlBulkInsert”, but as you understand, that’s just too long for a title.
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There is a common need in reporting to aggregate or return data that is crunched based on date attributes. These may include weekdays, holidays, quarters, or time of year. While any of this information can be calculated on the fly, a calendar table can save time, improve performance, and increase the consistency of data returned by our important reporting processes.
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In my previous two articles on SQL Server integration Services (SSIS), Parameterizing Database Connection in SSIS and Deploying Packages to SSIS Catalog (SSISDB), packages were developed, deployed and configured in the SSIS Catalog. Now, it is time to execute the packages with various options. There are a couple of ways to do this, but we need to be able to change the parameter values as well as monitor for failures or successes.
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CHECKSUM is an option for page verification that is available at the database instance level and we can see what level of verification each of our databases are currently using by the following query:
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Starting with SQL Server 2016 and Windows Server 2016 there have been numerous cloud related enhancements, most of them are tightly related to Microsoft’s Azure like the ‘Cloud Witness’ but there are others that can be implemented within other public or private clouds.
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SQL Server 2016 is the most advanced version of Microsoft’s Data Platform released yet. This is obviously my favorite one as it has tremendous capabilities and enormous features. These new additions not only enhance the productivity of its users (Database Developer, DBA or Application Developer), but also enable the enterprise to use its data more effectively and efficiently.
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Standalone Managed Service Accounts, introduced a long ago with Windows Server 2008 R2, were a ray of hope for the database administrators. They promised to provide automatic password management and simplified SPN management, meaning that the time-consuming task of maintaining passwords would be a thing of the past (not to mention the required downtime for this).
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Microsoft Azure SQL Database is a managed cloud database for programmers/developers to develop their products easier due to less involvement in maintenance allowing the customer to concentrate on their products, because, most of the data maintenance or infrastructure is taken care by the cloud service providers.
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SQL Server 2016 obtains the values of the date and time using the GetSystemTimeAsFileTime() Windows API, with precision fixed at 100 nanoseconds. The accuracy of these date and time values depends on the hardware specs and the version of the Windows of the server on which the SQL Server instance installed.
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SQL Server retrieves data from two areas; memory and disk. As disk operations are more expensive in terms of IO which means they are much slower SQL stores and retrieves data pages from an area known as the Buffer Pool where operations are much faster.
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By all accounts, the introduction of SQL Server Data Tools (SSDT) in SQL Server 2012 was a watershed moment for many SQL Server developers. For better or for worse, SSDT as an IDE for business intelligence development changed – amongst other things – the way we deployed our SSIS packages (i.e. package vs project deployments), simplified Tabular Model development, and also introduced us to the SSISDB. Likewise the replacement of Business Intelligence Development Studio (BIDS) with SSDT had its detractors who were noticeably not very happy that in addition to installing SQL Server 2012 you still had to do a separate download and installation of BI templates for SSDT (previously, BI templates in BIDS were available as soon as you installed SQL Server 2005/2008). Although SSDT-BI is still being offered as a separate installation, subsequent releases of SSDT have included several enhancements changes that should go a long way to winning the hearts of its critics. In this article we conduct a comparative analysis of all versions (up until 16.5) of SSDT and identify all the major improvements that have been introduced in the BI templates.
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As you may already have figured out, the default settings in SQL Server are not always the best. Such is the case when you are working with new user databases; usually you get a single data (*.mdf) and transaction log (*.ldf) file. The data file resides within the PRIMARY file group; the only one we have so far, and it will store all of our databases objects, system objects, user tables, user stored procedures and all other objects. In some cases this default configuration may be good enough for you, but let us cover why would you prefer a different configuration in your production environment.
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A SQL Server Computed Column is a virtual column that is not stored physically on the table, unless it is previously specified as PERSISTED. A computed Column value is calculated using a specific expression that can be constant, function, data from other columns on the same table or a combination of these types connected together by one or more operators.
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