Quick and dirty CLI for bulk extracting data from Sage Intacct and saving in Azure storage. Save them in a .env file for debugging or use export from the terminal to declare them (I'm using Ubuntu running in WSL2).
This code will run most effectively in a virtual environment. Set one up by:
- Running
make setupfrom the top directory of the project to create a virtual environment - Running
source ~/.SageCLI/bin/activateto activate the virtual environment - Running
make installto install the package dependencies
The following environment variables are required for the tool to work properly:
| Variable Name | Purpose |
|---|---|
| SAGE_USER_PASSWORD | API user password for connecting to Sage Intacct |
| SAGE_COMPANY_ID | API company id for connecting to Sage Intacct |
| SAGE_USER_ID | API user id for connecting to Sage Intacct |
| SAGE_SENDER_ID | API sender id for connecting to Sage Intact |
| SAGE_SENDER_PASSWORD | API sender password for connecting to Sage Intacct |
| AZURE_STORAGE_ACCT_NAME | Azure storage account name that you want to save the data in |
| AZURE_STORAGE_ACCT_KEY | Azure storage account access key (with no prefixes) |
| AZURE_STORAGE_FILESYSTEM | Azure storage account container you want to save the data in (will create a Sage_Intacct/data_download subdirectory in this container) |
From the working directory, run python3 sage_slasher.py --help to see the list of commands.
Run python3 sage_slasher.py {command-name} to start an operation. You will be prompted for information like the entity you want to extract or the data range that needs to be pulled.
Data Entities: Entity names mimic object parameter names in the Sage Intacct docs.
Date Ranges: Date ranges are exclusive on the right-hand side because it made it eay to handle varying number of days in months. If you want data for all of November 2022, use a start date of 2022-11-01 and an end date of 2022-12-01.
A log of all events is saved locally in the sage.log file. If the full-extract command is used, an additional .csv file will be generated, stamped with the entity and start time of the run containing summary statistics for each month. This can be helpful to know where the start if a long-running operation breaks halfway through due to a connection failure.