Use Parameters on Snowflake Queries with the Python Connector
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- Опубликовано: 14 окт 2024
- Parameterized queries are very important in the world of data engineering, and it is no different when you’re connecting to your Snowflake database. Luckily, the Snowflake Connector for Python gives us two great ways to use parameters in our queries. These methods will help guard against SQL injection attacks, and will make your queries more robust and efficient. Let’s look at our qmark and numeric parameter styles for querying Snowflake today!
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Use Parameters on Snowflake Queries with the Python Connector
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• Use Parameters on Snow...
Thanks so much! Great playlist
Welcome! Glad you like it.
Very good explanation, for i’ve a scenario where I’ve to define multiple snowflake data names that I have to use in my query how do I define multiple database??
USE Database db1, db2, db3..
Good question! You can create several databases and switch between them. You can see some syntax in this video:
ruclips.net/video/yvdwKpm1P7A/видео.html
Great video!
What is the advantage of using the pandas connector for snowflake?
Edit: Could we do a similar parameterization using a redshift instance/connector?
Thanks! I think the advantage with using the Python connector for snowflake is that there are some neat functions built-in that will use things like storage for staging to make things more efficient.
However, it is not totally necessary to use the connector; in fact, you can access Snowflake using ODBC and run many of the same commands, pull data directly to pandas, etc. Also, you can use ODBC for Redshift as well.
ODBC supports parameterization of the first type (1 of 2 in this video) using the ? character. So, to answer your question, the answer is yes, you can use very similar syntax with ODBC to provide safe parameterized queries against Redshift.
I will be covering ODBC on snowflake in a later video! You can see an example of Python with ODBC here (Oracle example):
ruclips.net/video/__pWyKvycjc/видео.html
@@seanmackenziedataengineering
I see. I am sort of new to snowflake but can see it can be a powerful tool for scalability and cross cloud platform capabilities.
Thank you so much for the information!
I am looking forward to that next video!
Thank you again sir. However I noticed that you used cs.execute() and passed the parameters. Is there a way to do the same with pandas e.g. in pd.read_sql() method can we parameterize our SQL query??
Thanks again in advance. Really appreciate all your videos
You can use cs.fetch_pandas_all() to retrieve the results into a pandas dataframe. If you're using Python with ODBC to Snowflake, you can also use read_sql with parameters like you say, but generally it is just easier with fetch_pandas_all() when using the connector.
Is it possible to parameterize the table names?
I don't think I have tried that! Let me know how it goes.
@@seanmackenziedataengineering It did not let me use parameters for table names. But I was able to use the python variable directly in the sql query.
sql_query = (f"select * from {object_name} limit 1")
cur.execute(sql_query)
@@nikhilm5896 I see!
Using Snowflake's identifier function, you parametrize table names:
snowflake.connector.paramstyle = 'qmark'
sql = ("select * from identifier(?)")
cur.execute(sql, ['table_name'])