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Python Data Loading from shopify to databricks using dlt Library

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This page provides technical documentation on how to use the open-source Python library, dlt, to load data from Shopify to Databricks. Shopify is a comprehensive commerce platform that empowers anyone to start, grow, manage, and scale a business. On the other hand, Databricks is a unified data analytics platform, developed by the original creators of Apache Spark™, that fosters innovation by integrating data science, engineering, and business. The dlt library serves as a bridge, allowing seamless data transfer between these two platforms. For more information on Shopify, please visit

dlt Key Features

  • Automated Maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more here.
  • Scalability and Efficiency: 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 here.
  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. This extraction DAG determines the optimal order for extracting the resources to ensure data consistency and integrity. Learn more here.
  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more here.
  • Flexibility of Destinations: dlt supports a wide range of destinations like Databricks, DuckDB, Google BigQuery, PostgreSQL, SQLite, and more. This flexibility allows you to choose the destination that best suits your data stack. Learn more 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 Databricks:

pip install "dlt[databricks]"

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

# 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 databricks
# 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── shopify_dlt/ # folder with source specific files
│ └── ...
├── # 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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

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

private_app_password = "private_app_password" # please set me up!
access_token = "access_token" # please set me up!

catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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, 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='databricks', dataset_name="shopify_data"
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='databricks', 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 =

# 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

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 =
start_date=max_end_date, created_at_min=min_start_date

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(

# 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 {
node {

# Configure the resource with the query and json paths to extract the data and pagination cursor
resource = shopify_partner_query(
# JSON path pointing to the data item in the results
# JSON path pointing to the highest page cursor in the results
# The variable name used for pagination

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:


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 Databricks 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: With dlt, you can utilize Github Actions as a CI/CD runner to deploy your pipeline. It allows you to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: dlt provides support for deploying your pipeline with Airflow, including Google's managed Airflow environment, Google Composer. It makes the process of creating an Airflow DAG for your pipeline script trivial.
  • Deploy with Google Cloud Functions: You can also deploy your pipeline with Google Cloud Functions using dlt. This serverless execution environment allows you to build and connect cloud services with code.
  • More Deployment Options: For more ways to deploy your pipeline with dlt, check out other deployment options available in the documentation.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt allows you to easily monitor your pipeline in production, providing you with useful information about the data loads and any issues that may have occurred. Learn more about it here.
  • Set Up Alerts: To ensure you are promptly informed about any critical issues in your pipeline, dlt offers an alerting feature. This allows you to set up alerts for a variety of situations, such as failed jobs or schema changes. Find out how to set up alerts here.
  • Set Up Tracing: dlt also provides a tracing feature, which allows you to track the execution of your pipeline and identify any potential bottlenecks or issues. You can learn 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 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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