Скачать или смотреть Resolving the Current Database Error in Snowflake Data Uploads with Python and Pandas

Learn how to fix the `current session does not have a current database` error when appending data to Snowflake using Python and Pandas. This guide includes solutions using SQLAlchemy and the `write_pandas()` method.
---
This video is based on the question https://stackoverflow.com/q/64991350/ asked by the user 'alim1990' ( https://stackoverflow.com/u/3922727/ ) and on the answer https://stackoverflow.com/a/64991768/ provided by the user 'alim1990' ( https://stackoverflow.com/u/3922727/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Python and Snowflake appending new data into an existing table using SQL Alchemy engine is returning current session does not have a current database

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving the Current Database Error in Snowflake Data Uploads with Python and Pandas

When working with Snowflake and Python, you may encounter a frustrating error that prevents you from successfully appending new data to an existing table. It typically arises when using SQLAlchemy together with the Pandas to_sql() method. The error message you see might read:

[[See Video to Reveal this Text or Code Snippet]]

The Root of the Problem

This error generally occurs when the Snowflake session does not have a designated database set, which is necessary for executing SQL commands such as creating tables or inserting records. When using SQLAlchemy to work with Snowflake, it’s critical to ensure that all connection parameters—including database name and schema—are properly specified and functional.

Step-by-Step Solution

Here's how to troubleshoot and resolve this issue, allowing you to append data seamlessly to your Snowflake table.

Step 1: Establishing Your Connection

Start by setting up your connection to Snowflake. As shown in the script below, ensure that you include the correct database, schema, and other required parameters in both your Snowflake connection and SQLAlchemy engine.

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Swapping to write_pandas()

Instead of utilizing the pd.DataFrame.to_sql() method, I recommend switching to write_pandas(), a function specifically designed for efficiently writing DataFrames to Snowflake. This approach circumvents the need for the SQLAlchemy engine and mitigates potential issues with current database settings.

Here’s how to implement this approach:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Installing Dependencies

Before you can use write_pandas(), make sure you have the pyarrow library installed. You can install it via pip:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Removing Unneeded Imports

If you transitioned to using write_pandas(), remember to simplify your imports to only include what's necessary:

[[See Video to Reveal this Text or Code Snippet]]

Final Thoughts

By following these steps, you should be able to bypass the current database error and successfully append new data to your existing Snowflake tables without further complications. Using write_pandas() often leads to a smoother experience in data uploads, eliminating many common pitfalls with traditional SQL expressions in Pandas.

For any additional issues or questions regarding your Snowflake and Python integration, feel free to reach out or explore more detailed documentation on the respective libraries.