CODEWITHSUNDEEP
pandassnowflake-cloud-data-platform
Ee Ann Ng
Ee Ann Ng

Reputation: 139

Can Snowflake run pandas manipulation?

I asked this question to chatGPT and got the following answer, I think it is wrong, but can someone confirm this too?

i dont want to load the data into my machine, rather i want the query and execution of the dataframe manipulation to be ran from snowflake engine itself, so that i don't consume my local machine's memory and cpu. can I do that with pandas on snowflake?

Answer from chatGPT:

Yes, you can execute Pandas dataframe manipulation directly in Snowflake using Snowpark without loading the data into your local machine's memory.

Snowpark allows you to use the Pandas API on data residing in Snowflake and execute the computation in the Snowflake engine. This means that the data remains in Snowflake, and you do not need to download or upload it to your local machine.

Here's an example of how to execute a Pandas dataframe manipulation in Snowflake using Snowpark:

import snowflake.connector
from snowflake.connector.pandas_tools import write_pandas

# Connect to Snowflake
conn = snowflake.connector.connect(
    user='<your_username>',
    password='<your_password>',
    account='<your_account>',
    warehouse='<your_warehouse>',
    database='<your_database>',
    schema='<your_schema>'
)

# Load data from Snowflake into a Pandas dataframe
df = pd.read_sql_query('SELECT * FROM my_table', conn)

# Perform data manipulation using Pandas API
df['new_column'] = df['old_column'] * 2

# Write the manipulated data back to Snowflake
write_pandas(conn=conn, df=df, table_name='my_table', schema='<your_schema>')

In the above example, the write_pandas function writes the Pandas dataframe df back to Snowflake, with the table_name and schema parameters specifying where to write the data.

By using Snowpark and the snowflake.connector.pandas_tools package, you can perform data manipulation using Pandas API on data residing in Snowflake and execute the computation in the Snowflake engine, without the need to load the data into your local machine's memory.

From what I understand, the Snowflake Connector is not related to Snowpark, and doing pd.read_sql_query is reading into local memory, am I wrong?

Upvotes: 0

Views: 250

Answers (1)

FirefoxMetzger
FirefoxMetzger

Reputation: 3260

You are correct. read_sql_query will pull all the data onto your local machine, do manipulation there, and then push the data back to snowflake via write_pandas.

If you want actual pushdown of pandas operators into Snowflake/SQL you could take a look at Ponder.io. They are a paid solution that does exactly this. Their founder is the author of the OOS project Modin which is a way to scale pandas out to a (ray-backed) server cluster.

Upvotes: 0

Related Questions