Why it Is Worth Upgrading to SQL Server 2016

Since its initial release, SQL Server has helped countless companies across myriad industries stay on track and get ahead of their competition using unmatched analysis, data storage, and security features.

Perhaps the most drastic update since its inception, Microsoft’s SQL Server 2016 now includes a more secure database, better performance, advanced analytics, and a slew of other newly introduced aspects.

Upgrading to SQL Server 2016 means having more opportunities for growth, less chances for SQL injection, and greater capacity for storing and optimizing data tables. Entities relying on older versions are not taking advantage of the full potential of this technology.

New Security Features Alone are Worth Making the Switch

For SQL Server 2016, Microsoft further built on Transparent Data Encryption (TDE), first released in SQL Server 2012, to ensure that data is always encrypted, whether at rest or in motion without impacting database performance. Not many users are aware their data is most vulnerable to theft when it’s in use, which is what makes this added security element so noteworthy. One simple data breach can have dire consequences, so why take the risk?

Another feature that made its debut with SQL Server 2016 is row-level security, which lets users control the specific records others can access in a database. This goes hand-in-hand with the next new feature, dynamic data masking, which allows users to set permissions and conceal specific data in real-time to protect sensitive information.

Performance and Scalability Enhancements Keep You Moving Forward

In SQL Server 2016, Microsoft revisited and improved upon several existing items, such as the in-memory Online Transaction Processing (OLTP), originally introduced in Microsoft’s SQL Server 2014, for more efficient data access and retrieval.

Query Store, a new feature introduced in SQL Server 2016, automatically captures analytics of the average runtime to find and fix performance issues, as well as how many times the query was executed. Query Store also retains previous query histories and execution plans for users to review.

The newest installment of SQL Server also includes a Stretch Database for seamless data transition between natively stored databases and the Cloud. This is especially helpful for businesses who are looking for alternative ways to store old data that rarely gets accessed. By setting up parameters to identify when data gets “cold,” Stretch Database will automatically migrate such data stored on native devices to Azure to optimize space in the database. The stored data can easily be accessed later and be kept online without having to go through expensive, time-consuming measures other current solutions offer.

With the debut of Temporal Tables in Microsoft’s SQL Server 2016, users can access and pull reports from data as it was previously in a specific point in time. By using this feature, businesses can study their historical data with greater accuracy to help make decisions.

Improvements to Business Intelligence (BI) and Analytics Offer Greater Convenience 

One of the new additions is the support for mobile reports. This is big as it allows business professionals the convenience of being able to share their reports and insights across an organization even when not in the office. The mobile reports run on the world’s most used operating systems including Windows, iOS, and Android.

Other upgrades include a new web portal that has a customizable interface, which incorporates mobile and paginated reports, Key Performance Indicators (KPI’s) and Power BI Desktop files. It also includes a major integration with R, the primary scripting language used for statistical analysis and data visualization. With R integration comes a few enhancements like advanced graphics and ease of transforming data. Users can also analyze data directly on the SQL Server database without having to move it.

Another feature that’ll make switching to SQL Server 2016 worth your while is PolyBase, which makes working with unstructured data easier. PolyBase minimizes the need to move data around by pushing queries to the external data source using standard T-SQL and only returning results. It allows users to store data on Azure blob storage as well as Hadoop to optimize room and preserve online for easy access. To enhance query performance, PolyBase makes use of this dual storage ability and pushes the computation over to Hadoop automatically with no additional software needed.

Always On Availability Groups Are There for You — Always

Microsoft first introduced Availability Groups in SQL Server 2012 to replace existing database mirroring technologies.

Database mirroring works by creating an exact replica of an existing database usually on a second SQL Server instance. If the first server instance fails, the user switches to the mirrored database in the second instance containing intact data.

Microsoft improved upon this high-availability and disaster-recovery feature with Always On Availability Groups in SQL Server 2016, which supports up to nine availability replicas, thus maximizing the availability of a set of databases for an enterprise. With this advanced feature, businesses can rest easy knowing their data is backed up and protected.

What to Do Next?

Companies relying on older versions of SQL Server now face a very important decision: either migrate to a more secure and better performing version of SQL Server, or remain with their legacy system. The cost of switching is minute compared to a loss of millions in revenue, potential lawsuits, and a damaged reputation should valuable data be compromised.

Despite the step-by-step installation guide provided by Microsoft, migrating to SQL Server 2016 comes with its own challenges. Especially for enterprises with a large number of data tables and small to midsized businesses who do not have in-house IT. To ensure the proper migration of database schemas and prevent data loss it is best to hire third-party software development companies. They not only ensure data integrity but also normalize data models by implementing relationships to minimize redundancies, optimizing the entire data environment.