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- #USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING INSTALL#
- #USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING UPDATE#
- #USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING CODE#
#USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING UPDATE#
If the connection is a one-time need, this is no big deal, but if you rely on the same connection string for connections in numerous notebooks, an update to the credentials might cause some serious tension headaches. Second, if the login credentials change, you will need to find and update every instance of the connection string in your Python files or Jupyter notebooks.
#USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING CODE#
Anyone with access to the code can discover the credentials quite easily, and if the code is maintained on publicly accessible version control site like GitHub, you could be giving your data away to anyone who wants it.
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But the hardcoding method presents some extremely serious issues.įirst, placing login credentials directly into the code presents a serious security issue. Hard-coding a connection string can be the simplest and fastest way to establish a database connection, and in an enterprise setting, speed can be highly valued. The pyodbc library can be tricky to install, so a bit of light googling might also be required.
#USE SQLALCHEMY WITH SQL SERVER CONNECTION STRING INSTALL#
If you do not already have these packages installed, you can install them using pip. If you are curious, sqlalchemy’s ‘create_engine’ function can leverage the pyodbc library to connect to a SQL Server, so we import that library, as well. The sqlalchemy engine works well with pandas data frame, so we will use those libraries to perform our SQL queries below. Getting Startedīefore we do anything, we will need to install some third-party Python packages to help us establish and use our connections.
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We will start with the least secure method – hardcoding credentials into the connection code – because it is easy to understand, but we will build toward our ultimate goal, a connection that is both secure and easy to use. We will also assume that there is an existing SQL user with all of the permissions required to access the database. For purposes of this tutorial, we will assume the database is stored on a Microsoft SQL Server, but the connection process should be about the same no matter what type of database management system you are using. Here we explore some methods for establishing a connection to a SQL database using Python in a Jupyter notebook. With the pandas library, extracting data from a SQL database in a Jupyter notebook is almost trivial, but before we can extract the data, we need to establish a connection to the database. SQL is everywhere, and if you are doing any sort of analysis in an enterprise setting, it is more likely than not that you will need to access a SQL database for at least some of your data.