Loading Data from Shopify to MotherDuck using Python and dlt

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Welcome to our technical documentation on how to load data from Shopify to MotherDuck using the open-source Python library, dlt. Shopify is a comprehensive commerce platform that empowers individuals to initiate, expand, manage, and scale businesses. On the other hand, MotherDuck, powered by DuckDB, is a rapid in-process analytical database with a rich SQL dialect and extensive client API integrations. By leveraging the dlt library, you can efficiently transport data between these platforms. For more details about Shopify, please visit https://www.shopify.com/.

dlt Key Features

• Automated Maintenance: With schema inference and evolution, alerts, and short declarative code, dlt simplifies maintenance tasks. It automates many of the routine tasks, freeing up your time to focus on more critical aspects of your project. Learn more
• Scalability: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Learn more
• Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This feature ensures data consistency and integrity. Learn more
• Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Learn more
• User-Friendly Interface: dlt provides a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. It is designed to be easy to use and understand, making it an ideal choice for both beginners and experienced professionals. Learn more

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for MotherDuck:

pip install "dlt[motherduck]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Shopify to MotherDuck. You can run the following commands to create a starting point for loading data from Shopify to MotherDuck:

# create a new directory
mkdir my-shopify_dlt-pipeline
cd my-shopify_dlt-pipeline
# initialize a new pipeline with your source and destination
dlt init shopify_dlt motherduck
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:

dlt[motherduck]>=0.3.8

You now have the following folder structure in your project:

my-shopify_dlt-pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── shopify_dlt/                   # folder with source specific files
│   └── ...
├── shopify_dlt_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline:

config.toml

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

[sources.shopify_dlt]
shop_url = "shop_url" # please set me up!
organization_id = "organization_id" # please set me up!

secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.shopify_dlt]
private_app_password = "private_app_password" # please set me up!
access_token = "access_token" # please set me up!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # please set me up!

Further help setting up your source and destinations

Please consult the detailed setup instructions for the MotherDuck destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Shopify source in the dlt verifed sources documentation.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at shopify_dlt_pipeline.py, as well as a folder shopify_dlt that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

"""Pipeline to load shopify data into BigQuery.
"""

import dlt
from dlt.common import pendulum
from typing import List, Tuple
from shopify_dlt import shopify_source, TAnyDateTime, shopify_partner_query


def load_all_resources(resources: List[str], start_date: TAnyDateTime) -> None:
    """Execute a pipeline that will load the given Shopify resources incrementally beginning at the given start date.
    Subsequent runs will load only items updated since the previous run.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify", destination='motherduck', dataset_name="shopify_data"
    )
    load_info = pipeline.run(
        shopify_source(start_date=start_date).with_resources(*resources),
    )
    print(load_info)


def incremental_load_with_backloading() -> None:
    """Load past orders from Shopify in chunks of 1 week each using the start_date and end_date parameters.
    This can useful to reduce the potiential failure window when loading large amounts of historic data.
    Chunks and incremental load can also be run in parallel to speed up the initial load.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify", destination='motherduck', dataset_name="shopify_data"
    )

    # Load all orders from 2023-01-01 to now
    min_start_date = current_start_date = pendulum.datetime(2023, 1, 1)
    max_end_date = pendulum.now()

    # Create a list of time ranges of 1 week each, we'll use this to load the data in chunks
    ranges: List[Tuple[pendulum.DateTime, pendulum.DateTime]] = []
    while current_start_date < max_end_date:
        end_date = min(current_start_date.add(weeks=1), max_end_date)
        ranges.append((current_start_date, end_date))
        current_start_date = end_date

    # Run the pipeline for each time range created above
    for start_date, end_date in ranges:
        print(f"Load orders between {start_date} and {end_date}")
        # Create the source with start and end date set according to the current time range to filter
        # created_at_min lets us set a cutoff to exclude orders created before the initial date of (2023-01-01)
        # even if they were updated after that date
        data = shopify_source(
            start_date=start_date, end_date=end_date, created_at_min=min_start_date
        ).with_resources("orders")

        load_info = pipeline.run(data)
        print(load_info)

    # Continue loading new data incrementally starting at the end of the last range
    # created_at_min still filters out items created before 2023-01-01
    load_info = pipeline.run(
        shopify_source(
            start_date=max_end_date, created_at_min=min_start_date
        ).with_resources("orders")
    )
    print(load_info)


def load_partner_api_transactions() -> None:
    """Load transactions from the Shopify Partner API.
    The partner API uses GraphQL and this example loads all transactions from the beginning paginated.

    The `shopify_partner_query` resource can be used to run custom GraphQL queries to load paginated data.
    """

    pipeline = dlt.pipeline(
        pipeline_name="shopify_partner",
        destination='motherduck',
        dataset_name="shopify_partner_data",
    )

    # Construct query to load transactions 100 per page, the `$after` variable is used to paginate
    query = """query Transactions($after: String, first: 100) {
        transactions(after: $after) {
            edges {
                cursor
                node {
                    id
                }
            }
        }
    }
    """

    # Configure the resource with the query and json paths to extract the data and pagination cursor
    resource = shopify_partner_query(
        query,
        # JSON path pointing to the data item in the results
        data_items_path="data.transactions.edges[*].node",
        # JSON path pointing to the highest page cursor in the results
        pagination_cursor_path="data.transactions.edges[-1].cursor",
        # The variable name used for pagination
        pagination_variable_name="after",
    )

    load_info = pipeline.run(resource)
    print(load_info)


if __name__ == "__main__":
    # Add your desired resources to the list...
    resources = ["products", "orders", "customers"]
    load_all_resources(resources, start_date="2000-01-01")

    # incremental_load_with_backloading()

    # load_partner_api_transactions()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python shopify_dlt_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline shopify info

You can also use streamlit to inspect the contents of your MotherDuck destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline shopify show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

• Deploy with Github Actions: Understand how to use the dlt deploy command to deploy your pipeline with Github Actions. This guide includes step-by-step instructions on how to specify when the GitHub Action should run using a cron schedule expression.
• Deploy with Airflow: Learn how to deploy a pipeline with Airflow and Google Composer. This guide provides a detailed walkthrough on how to add your dlt project directory to GitHub, ensure your pipeline works, and initialize deployment.
• Deploy with Google Cloud Functions: Discover how to deploy a pipeline with Google Cloud Functions. This guide gives you step-by-step instructions on how to prepare your pipeline for deployment using Google Cloud Functions.
• Other Deployment Options: Explore other ways to deploy your pipeline with dlt. This guide provides links to various deployment options and their corresponding guides.

The running in production section will teach you about:

• Monitor Your Pipeline: dlt provides a comprehensive guide on how to monitor your pipeline. The guide includes tips on inspecting and saving load info, accessing runtime trace, and alerting on schema changes. Learn more here.
• Set Up Alerts: It's crucial to set up alerts to stay informed about the status of your pipeline. dlt makes this process straightforward with detailed instructions and examples. Check out the guide here.
• Set Up Tracing: Tracing is an essential aspect of running a dlt pipeline in production. It provides timing information on extract, normalize, and load steps, among other useful data. Find out how to set up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source shopify

Shopify is an e-commerce platform offering data on customer accounts, transactions, and product listings.

Resource Name	Write Disposition	Description
customers	merge	Individuals or entities who have created accounts on a Shopify-powered online store
orders	merge	Transactions made by customers on an online store
products	merge	The individual items or goods that are available for sale

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