Introduction to Python SQL Server
With the common usage of Python programming language and SQL Server as a database for almost all the transactions, the connection of Python and SQL Server is very much important to do all the Machine Learning activities. It helps to do all the manipulations to the data with the help of Python programs and SQL queries so that Machine Learning activities can be done easily in the system and to the data. Connection string details along with server and database names should be given in the command-line interface to do the connections.
Why do we need Python SQL Server?
- When all the data science programs are being written in Python, and when there is a need of queries to check the data present in the database, it is tedious to run the program in one application and check the data count in another application. Hence, it is important to link both Python and SQL servers to get all the work done in a short span of time. This helps to save time for data extraction as it can be run directly in the server.
- When data is taken outside the database, security and compliance have to be checked with the second application with which it will be transferred. This must mean that the data moved should be in line with all other security, integrity, and capability applications. Also, we can do a sample test in the database directly without taking it to the Python application to test the samples. This is because the database network works better than Python, so the data wrangling happens faster than in Python. This is another reason for selecting it.
- SQL Server supports all types of integration with Python through extensions and libraries. While using SQL Server, all the advantages such as indexes and in-memory tables can be considered for data manipulation. This makes data retrieval easy for all the applications within SQL Server. Faster computations with less network traffic also help data to be analyzed and given to Python for further processing. We can write Python codes directly in the server and analyze the data.
How does Python SQL Server work?
- Download and install SQL Server in the system. While doing the initial setup, we should select Python from Machine Learning Services in the application. Next, database engine services and machine services should be selected during feature selection, where Python comes under machine services. Finally, we have two options, Python and R, where Python should be selected as we need to run Python in SQL Server. Next, select ‘consent to install Python’ after which Python will be installed along with SQL Server.
- After installing SQL Server Management Studio and Python language, run sp_configure to run external scripts in SQL Server. Then, after restarting SQL instance once, run system Stored Procedure ‘sp_execute_external_script’ once with Python script as an argument. This helps to run Python in SQL Server along with the supported data types in Python. Data types will be converted directly into Python supportive data types and others which does not have Python support; we should convert manually into Python supportive datatypes. Finally, we can read the csv file using the Pandas library and do the statistical calculations in the table.
- We should install pyodbc using the PIP package. Assuming that we know the server name, database and table name, the below code should be written in Python by filling in the relevant details and deployed. We can also use Pandas data frame along with connection string ‘connection’ to connect with the data frames and SQL directly.
import pyodbc
connection = pyodbc.connect('Driver={SQL Server}'
'Server=servername;'
'Database=databasename;'
'TrustedConnection=yes;')
Cursor_exe = connection.cursor()
Cursor_exe.execute('SELECT * FROM database_name.table')
for row in cursor_exe:
print(row)Once this connection is completed, we can write Python codes in SQL Server and do the data analysis.
Advantages and Disadvantages of Python SQL Server
Given below are the advantages and disadvantages mentioned:
Advantages
- Data movement from one application to another is reduced, resulting in more security and governance and good performance in the data analysis. In addition, we can use Python extensions in SQL Server to manage the data inside the server itself. With a simple Stored Procedure, we can do deployments easily in the database, and we can run Python codes easily in the system, similar to running T-SQL scripts.
- Any Python extensions can be easily installed and used along with T-SQL scripts. This helps to build any deep learning or AI applications in SQL server without any troubles. The integration of Python with SQL server can be done easily, and no extra charge is levied much to the satisfaction of users. We can scale Python applications easily with the available extensions and SQL queries making it good to work with all the latest advancements in the application. Data engineers, data scientists and database administrators can use this functionality and improve the performance of the application.
Disadvantages
- SQL has some limitations in data manipulation as it works only with tables and available indexes in the tables. Regarding Python, it is not necessary that it has tables, but we can do data manipulation of any sort in Python with any formats and do regression tests. Furthermore, we can also do data manipulation based on time series. Hence, the combination of both will help developers to do data analysis in any format.
- Python developers might find it difficult to understand the interface of the SQL Server as it is not easy as a Python application. Features must be known beforehand to use it along with SQL Server. Some might find SQL Server Management Studio costly as Python is offered as an open-source to everyone. If one is familiar with SSMS and there is funding for the application, SSMS and Python are good choices.
Conclusion
It is important to know SQL so that we can use all the SQL tricks in Python codes and make the work easy for all. This integration helps in all advanced data science techniques to be used in SQL Server with better performance.
