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Loading Data from Shopify to The Local Filesystem using dlt in Python

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Loading data from Shopify to The Local Filesystem using dlt is a straightforward process. Shopify is a comprehensive commerce platform that enables users to start, grow, manage, and scale their businesses. On the other hand, The Local Filesystem destination allows you to store data in a local folder, making it easy to create data lakes. You can save data in various formats such as JSONL, Parquet, or CSV. Using the open-source Python library dlt, you can efficiently extract data from Shopify and load it into The Local Filesystem. For more details about Shopify, visit here.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance capabilities through pipeline metadata, schema enforcement, and schema change alerts. Learn more.
  • Scalable Data Extraction: Leverage iterators, chunking, and parallelization techniques for efficient processing of large datasets. Learn more.
  • Schema Enforcement and Curation: Ensure data consistency and quality by enforcing and curating schemas. Learn more.
  • Implicit Extraction DAGs: Automatically handle dependencies between data sources and transformations with extraction DAGs. Learn more.
  • Configurable Normalization Engine: Recursively unpack nested structures into relational tables, making data ready to be loaded. 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 The Local Filesystem:

pip install "dlt[filesystem]"

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

# 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 filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!

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 The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem', 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='filesystem', 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='filesystem',
        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 The Local Filesystem 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: Learn how to deploy your dlt pipeline using GitHub Actions for a free CI/CD runner. Follow the guide here.

  • Deploy with Airflow and Google Composer: Discover how to deploy your dlt pipeline using Airflow and Google Composer. Detailed instructions can be found here.

  • Deploy with Google Cloud Functions: Follow this guide to deploy your dlt pipeline using Google Cloud Functions. Check out the steps here.

  • Explore other deployment options: For more deployment options and detailed walkthroughs, visit the comprehensive guide here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth and error-free operations. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified about any issues or important events in your dlt pipeline, helping you to take immediate action. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the performance and execution flow of your dlt pipeline, aiding in debugging and optimization. And 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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