With Microsoft updating SQL with a brand new range of features, the game in database management is about to change – a lot. Announcing a grand variety of next-generation features in SQL 2017, the tech giant has introduced a range of additions to its latest data manipulation program; including highlights such as support for Python and Native R programming – one of the many exciting additions.
Here we’ll break down five SQL updates that you need to know about in 2017.
Cross-platform support now available
Great news for Linux and Mac aficionados – SQL can now run on all three of the most popular GUIs on the market. It’s available for full download on Linux, or it can be booted on Docker to run on macOS.
This is fantastic news for organizations that don’t run Microsoft; who are desperate for a database management platform that integrates with Windows.
Enhanced security at the data layer
While there are many organizations that offer SQL database recovery, organizations now have enhanced protection available directly on the data layer. This makes storing and organizing data with popular tools such as MySQL that extra bit more secure. On top of Dynamic Data Masking, Always Encrypted and Row Level Security introduced in SQL 2016 is the ability to secure columns, as well as rows.
Addition of machine learning features
Any business that is looking to take full advantage of advances in machine learning can use Python and R programming languages which have proven incredibly successful in the field of AI. Data scientists now have the potential to leverage any existing libraries to generate new ones in the current system.
This, as you might guess, could prove to be extremely important, as there is now no need to support many tool sets to succeed advanced analytic aims with fresh data.
BI features around analytics improved
Analysis services have also been improved, which are generally used to process large sets of data in the attempt to aggregate. A vast range of updated features have been added, including:
- Enhanced support for ragged hierarchies
- Mashups with Power Query Formula Language
- Data transformation features
- Improved time intelligence for data/time dimensions used
Store and manage intelligent data
Changing the way we browse data, SQL 2017 has changed the game completely. Organizations will now be able to implement various algorithms, allowing them to retrieve data and look at anything that has already been analyzed and processed. By integrating its AI capabilities alongside its latest SQL server engine, allows the production of much more intelligent data.
What does this mean overall then?
All clients can recognize that developing a strategy centered on business intelligence requires great investment – in money and time. From data acquisition, management, applying algorithms and then passing it through visualization tools can, at times, be a long and complicated process. And so consolidating all the above – just as Microsoft is proposing – has the potential to streamline the process of gaining insight around data, allowing businesses to gain quick access to AI much easier than before.