In the last article in this series about Python for SQL Server specialists, you are going to learn how to use SQL Server Python libraries in SQL Server. You can use two scalable libraries, the revoscalepy and microsoftml libraries, which correspond to equivalent R libraries.
SQL Server and ML Integration
With SQL Server 2016 and 2017, you get a highly scalable Machine Learning (ML) engine. Not every function and algorithm is rewritten as a scalable one. Nevertheless, you will probably find the one you need for your analysis of a big dataset. You can store a Python or R data mining or machine learning model in a SQL Server table and use it for predictions on new data. You can even store graphs in a binary column and use it in SQL Server Reporting Services (SSRS) reports. Finally, ML support is not limited to SQL Server only. You can use R code also in Power BI Desktop and Power BI Service, where we at this time (April 2018) still waiting for Python support. However, you can already use both languages, Python and R, in Azure Machine Learning (Azure ML) experiments. (more…)
After learning about Python fundamentals and basics about working with data, it is time to start with more exciting parts of this Python for SQL Server Specialists series.
In this article you will learn about the most important libraries for advanced graphing, namely matplotlib and seaborn, and about the most popular data science library, the scikit-learn library. (more…)
In my previous article, you learned Python fundamentals. I also introduced the basic data structures. You can imagine you need more advanced data structures for analyzing SQL Server data, which comes in tabular format. In Python, there is also the data frame object, like in R. It is defined in the pandas library. You communicate with SQL Server through the pandas data frames. But before getting there, you need first to learn about arrays and other objects from the numpy library.
In this article, you will learn about the objects from the two of the most important Python libraries, namely, as mentioned, numpy and pandas. (more…)
Python is one of the most popular programming languages. It is a general purpose high level language. It was created by Guido van Rossum, publicly released in 1991. SQL Server 2016 started to support R, and SQL Server 2017 adds support for Python. Now you can select your preferred language for the data science and even other tasks. R has even more statistical, data mining and machine learning libraries, because it is more widely used in the data science community; however, Python has broader purpose than just data science, and is more readable and might thus be simpler to learn. This is the first of the four articles that introduce Python to SQL Server developers and business intelligence (BI) specialists. This means that the articles are more focused on Python basics and data science, and less on general programming with Python.
After years of blogging at SolidQ site, I decided to move my blog here, to SQLBLOG.COM. It seems that most of the SQL Server specialists like to hang around together. In addition, blogging on a specific company’s site is not too popular because of possible limitations; independent sites are preferred. Instead of competing with my friends, I decided to join them 🙂
Here is the list of the fall 2013 conferences where I am going to attend and speak, or have a pre-conference seminar. Of course, if you are interested in this list. If not, you do not need to stop reading now. It is still a list of good conferences, even if you don’t want to listen to me. And if you attend, you will definitely listen to me. If not during the sessions, you will not be able to miss me during evening eventsJ
Because SQL Server does not have any support for temporal data out of the box, there are always problems with this kind of data. The most important problem is the performance. Traditional solutions have to deal with slow queries, which also make slow constraints implemented through triggers. (more…)