Load inFlow Inventory data in Python using dltHub

Build a inFlow Inventory-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete InFlow Inventory data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def inflow_inventory_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloudapi.inflowinventory.com/v1/",
            "auth": {
                "type": "apikey",
                "token": api_key,
            },
        },
        "resources": [
            "adjustment_reasons",
            "categories",
            "products"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='inflow_inventory_pipeline',
        destination='duckdb',
        dataset_name='inflow_inventory_data', 
    )
    # Load the data
    load_info = pipeline.run(inflow_inventory_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from inflow_inventory’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Adjustment Reasons: Retrieve the reasons for adjustments made in inventory.
  • Categories: Access the various categories defined in the inventory system.
  • Currencies: Get the list of currencies supported by the system.
  • Customers: Fetch customer details from the inventory database.
  • Locations: Access information about different locations managed in the system.
  • Operation Types: Retrieve types of operations performed on inventory items.
  • Payment Terms: Get the payment terms available in the system.
  • Pricing Schemes: Access the pricing schemes defined for products.
  • Products: Retrieve product details from the inventory.
  • Purchase Orders: Fetch details of purchase orders.
  • Sales Orders: Access sales order information.
  • Stock Adjustments: Retrieve details about adjustments made to stock levels.
  • Stock Counts: Access stock count information.
  • Stock Transfers: Get details on transfers between stock locations.
  • Tax Codes: Retrieve tax codes used in transactions.
  • Taxing Schemes: Access different taxing schemes defined in the system.
  • Team Members: Fetch information about team members managing the inventory.
  • Vendors: Retrieve details of vendors associated with the inventory.

You will then debug the InFlow Inventory pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with InFlow Inventory support.

    dlt init dlthub:inflow_inventory duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for InFlow Inventory API, as specified in @inflow_inventory-docs.yaml 
Start with endpoints "adjustment_reasons" and "categories" and skip incremental loading for now. 
Place the code in inflow_inventory_pipeline.py and name the pipeline inflow_inventory_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python inflow_inventory_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The authentication for this source is done via API Key. Users must provide their API key in the request headers to access the endpoints.

    To get the appropriate API keys, please visit the original source at https://www.inflowinventory.com/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python inflow_inventory_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline inflow_inventory load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset inflow_inventory_data
The duckdb destination used duckdb:/inflow_inventory.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline inflow_inventory_pipeline show --dashboard

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("inflow_inventory_pipeline").dataset()
# get "adjustment_reasons" table as Pandas frame
data.adjustment_reasons.df().head()

Running into errors?

No pagination is supported for any streams, which may limit the amount of data retrieved in a single request. The API supports full sync but does not allow for incremental loading. It's important to check API keys and company IDs to avoid 401 Unauthorized errors, and verify endpoint paths and parameters to avoid 404 Not Found errors.

Extra resources:

Next steps