On the 19th of April 2017, Microsoft held an online conference called Microsoft Data Amp to showcase how Microsoft’s latest innovations put data, analytics and artificial intelligence at the heart of business transformation. Microsoft has, over the last few years, made great strides in accelerating the pace of innovation to enable businesses to meet the demands of a dynamic marketplace and harness the incredible power of data—more securely and faster than ever before.
After the conference, there were a few questions some of us had, though. Is Microsoft SQL Server 2017 emerging as an enterprise solution for data science? Does it provide the required capabilities—is the engine capable of handling huge data? It seems the answer is “Yes”, as starting with the CTP 2.0 release of SQL Server 2017, Microsoft has brought Python-based intelligence to data in SQL Server.
Python has gathered a lot of interest recently as a language of choice for data analysis. This language has the right set of libraries for data analysis and predictive modeling, not to mention a simpler learning curve.
The growing trends of data science and modeling predict a massive growth in data in the upcoming years. The propulsion towards innovation and adaptation to leading trends in the data technology might intrigue us enough to make us take a look at the current release of SQL Server 2017.
Data science is a combination of Data Mining, Machine Learning, Analytics and Big Data. The integration of SQL 2016 with data science language, R, into the database the engine provides an interface that can efficiently run models and generate predictions using SQL R services. Python builds on the foundation laid for R Services in SQL Server 2016, and extends that mechanism to include Python support for in-database analytics and machine learning.