Notable Improvements Made in SQL Server 2017
Microsoft is an organization that prides itself in its commitment to constantly improving its products over time. Once again, Microsoft’s SQL Server – a relational database management system – is in the spotlight, and even though it has only been one year since the release of the previous version, it still seems as if it has already been dramatically improved.
The main purpose of SQL Server (which first debuted in 1989) is to make the process of retrieving and storing data as efficient as possible. This is especially important for larger organizations who do not want to be functionally limited by their speed nor storage capacity. Keeping these primary purposes in mind, the engineers at Microsoft sought to improve the 2017 version by focusing on the use of Cloud services and increasing the number of possible operating platforms.
Here is a quick look at some of this year’s most progressive changes:
Now supporting Python and Linux
One of the primary criticisms of the 2016 version of the SQL Server by those who are currently working deep in the tech industry is that it was unable to support Python and Linux. This caused many individuals who rely on these two platforms to begin looking elsewhere for data storage options. However, this is simply no longer the case—now that the 2017 version of SQL Server is compatible with both Python and Linux, it is quickly proving itself to be the most flexible and wide-reaching model to date.
Computer scientists everywhere have often taken note of just how compatible Windows and Linux products can be. This compatibility has been particularly noteworthy when it comes to analyzing certain types of data, building online indexes, and numerous other functions. Now, more than ever, these otherwise competing platforms can be united as one under the common parity provided by the newest version of SQL Server.
Improved utilization of Microsoft cloud functions
There is no secret that Microsoft has been actively trying to shift its data storage and retrieval operations to the use of the cloud. Cloud-based networks have proven to be just as secure as more traditional options while also being able to transcend the typical limitations that exist when it comes to storing data.
As Microsoft continues to integrate SQL Server into a cloud based system, the storage and retrieval of different data sets will continue to become more functional than ever before. The use of Azure and other cloud-related progress has clearly established Microsoft as a company that is prepared to keep moving forward. In a relationship database management system such as that provided by Microsoft’s SQL Server, it is very important to continually be innovating—the increased utilization of the cloud is a perfect demonstration of why Microsoft has unquestionably reigned as the industry leader.
Introduction of graph database
As time has gone on, databases have generally become much more complex. One notable improvement that can be witnessed in SQL Server 2017 is the inclusion of a graph database within the core engine itself. Graph databases make it much easier to store, display—and consequently both understand and explain—various types of data.
After all, a graph is functionally just a demonstration of a relationship between multiple variables, which is exactly what a relationship database management system ought to be able to do. The inclusion of a graph database will dramatically increase the usefulness of stored data, and is undeniably a much welcomed improvement.
When it comes to database administration, it is very important to be able distinguish which sets of data are the most important, which are most likely to be needed, and which ways the database can deliver such information more efficiently. It would be highly inefficient (and simply wrong) to just suppose that all data had an equal chance of being used. Not all data is created equal, and there is a natural hierarchy that exists between that which will very likely be needed and that which will very unlikely be needed.
Automatic tuning is a continuous process that helps analyze data usage and construct the most efficient possible hierarchy among the various data points being stored. This leads to increased functionality across the board. By continuously cycling between the processes of learning, adapting, and verifying, SQL Server can now actively be becoming “smarter” as time goes on, and thus become dramatically more efficient.
Adaptive query plans
The primary way in which databases can really begin to distinguish themselves from one another is by their ability to perform over time. As time goes on, the most adaptive databases will continue to be able to gain a greater advantage over their competitors, and thus, the investment in a quality database is one that is compounding in value.
The adaptive query optimization that can be found in SQL Server 2017 enables it to utilize the query’s history and current performance in a way that directly increases future efficiency. While this sort of feature had already been introduced into the 2016 model, it has been dramatically improved, and now, query hierarchies are able to be automatically reordered more effectively than ever before.
There are a number of reasons SQL Server 2017 has gradually been establishing itself as the pride of the entire Microsoft fleet of products. Its commitment to efficiency, comprehensive solutions, innovation, and constant improvement make it clearly the best relationship database management system on the market.
Users are now able to organize larger amounts of data in a more secure medium while simultaneously being able to access the data they immediately need to be using more effectively than ever before. Though there are still certainly a number of things that Microsoft can continue to expand and improve upon when it comes to the next edition of SQL Server, this most recent model undeniably demonstrates a number of essential improvements and has proven itself to be well-deserving of the ample praise it has already received.
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