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Loading Shopify Data to PostgreSQL Using Python's dlt Library

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This page provides technical documentation on how to load data from Shopify, a comprehensive commerce platform, to PostgreSQL, a robust open-source object-relational database system. The process involves using dlt, an open-source Python library, to facilitate the data transfer. Shopify enables anyone to start, manage, and scale a business, while PostgreSQL offers features that safely store and scale the most complex data workloads. With dlt, you can effectively bridge these two platforms. For more information on Shopify, please visit https://www.shopify.com/.

dlt Key Features​

  • Install dlt with PostgreSQL: The DLT library is easy to install with PostgreSQL dependencies. You just need to run pip install dlt[postgres]. More details can be found here.
  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about this here.
  • Data Types: dlt supports a variety of data types like text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, wei. More details can be found here.
  • Extracting data with dlt: Extracting data with dlt is simple - you simply decorate your data-producing functions with loading or incremental extraction metadata, which enables dlt to extract and load by your custom logic. More about this can be found here.
  • How dlt works: dlt automatically turns JSON returned by any source (e.g. an API) into a live dataset stored in the destination of your choice (e.g. Google BigQuery). It does this by first extracting the JSON data, then normalizing it to a schema, and finally loading it to the location where you will store it. More about this can be found here.

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 PostgreSQL:

pip install "dlt[postgres]"

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 PostgreSQL. You can run the following commands to create a starting point for loading data from Shopify to PostgreSQL:

# create a new directory
mkdir shopify_dlt_pipeline
cd shopify_dlt_pipeline
# initialize a new pipeline with your source and destination
dlt init shopify_dlt postgres
# 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[postgres]>=0.3.8

You now have the following folder structure in your project:

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. The automatically created version of these files look like this:

generated 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!

generated 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.postgres.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 5432
connect_timeout = 15

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Shopify source in our docs.
  • Read more about setting up the PostgreSQL destination in our docs.

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='postgres', 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='postgres', 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='postgres',
        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 PostgreSQL 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: dlt provides a way to deploy your pipeline using Github Actions. This is a CI/CD runner that you can use basically for free.
  • Deploy with Airflow: You can also deploy your pipeline with Airflow. Google Composer is a managed Airflow environment provided by Google that dlt supports.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: Apart from the above, dlt supports other deployment options too. You can choose the one that best suits your project requirements.

The running in production section will teach you about:​

  • Monitor Your Pipeline: It's essential to keep an eye on your pipeline's performance and status. dlt provides comprehensive monitoring tools that help you track your pipeline's progress and identify any issues. Learn more about How to Monitor your pipeline.
  • Set Up Alerts: Stay informed about your pipeline's status with dlt's alerting feature. You can set up alerts to notify you of any changes or issues with your pipeline. Find out more on how to Set up alerts.
  • Enable Tracing: Tracing allows you to track the execution of your pipeline and identify any bottlenecks or issues. dlt offers robust tracing tools to help you optimize your pipeline's performance. Discover how to set up tracing.

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 NameWrite DispositionDescription
customersmergeIndividuals or entities who have created accounts on a Shopify-powered online store
ordersmergeTransactions made by customers on an online store
productsmergeThe individual items or goods that are available for sale

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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