Generate Temp Table SQL

Generate Temp Table SQL is a Python package that generates SQL statements for creating a temporary table and inserting data from a CSV file. It's useful when you need to move data between disconnected databases and data warehouses. You can now simply unload a CSV, turn that CSV into SQL statements creating a temp table and inserting data with a CLI command, and copy those SQL statements into your query editor so you can start using the data in a different warehouse.

Why did I build this? I often need to move data between disconnected warehouses. E.g. when I need to join data in my finance warehouse with data in my operational warehouse to see how a feature is linked to customer spend. I can use excel to convert a CSV to SQL statements...but it's a PITA.

With generate_temp_table_sql, I can now move the data in seconds. I simply download the CSV, run generate-tt-sql csv_name.csv in my terminal, and copy it into my query editor. It's immediately available to query so I can go straight to analysis.

If you have the same problem, this will save you a ton of time!

Also available in PyPi.

Features

• Load data from a CSV file
• Generate a CREATE TEMP TABLE SQL statement
• Generate INSERT INTO SQL statements for the data
• Command-line interface (CLI) for easy usage

Installation

Prerequisites

• Python 3.6 or higher
• pandas library

Installing

1. Clone the repository:

   git clone https://github.com/rywaldor/generate_temp_table_sql.git
   cd generate_temp_table_sql
   

2. Install the package locally:

   pip install -e .
   

Usage

Command-Line Interface (CLI)

After installing the package, you can use the generate-tt-sql command to generate SQL statements from a CSV file.

Basic Usage

To generate SQL statements and save them to a file:

generate-tt-sql path/to/your/file.csv

Additional Options

--o The path to the output SQL file.  Defaults to the director you call the command in.
--overwrite: Allow overwriting the output file if it exists.
--table_name: Specify the name of the temporary table to create.
--column_type: Specify the data type of the columns in the temporary table. Defaults to TEXT which works for Redshift and Snowflake. Use STRING for BigQuery.
--batch_size: Specify the number of rows inserted per insert statement.  If present, it creates multiple insert statements based on the batch size specified.

Example

Assume you have a CSV file example.csv with the following content:

name,age,city
John,30,New York
Jane,25,Los Angeles

Run the following command to generate SQL statements:

generate-tt-sql example.csv -o output.sql --table_name my_temp_table --column_type STRING

The output.sql file will contain:

CREATE TEMP TABLE my_temp_table (
    name STRING,
    age STRING,
    city STRING
);

--Insert Data SQL:
INSERT INTO my_temp_table (name, age, city) VALUES 
    ('John', '30', 'New York'),
    ('Jane', '25', 'Los Angeles');

All arguments are optional except the csv

Running Tests

To run the tests, use the following command:

python -m unittest discover -s tests

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Ryan Waldorf - ryan@ryanwaldorf.com
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