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Using Python's dlt to Load Data from Rest API to Databricks

About our rest_api verified source

This example demonstrates how to use the rest_api to retrieve data from the GitHub Rest API, but will work with any HTTP Rest API. Please read:

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This documentation provides a guide on how to load data from a Rest API into Databricks using an open-source Python library called dlt. Rest API is a verified source that supports data extraction from any HTTP rest API. On the other hand, Databricks is a unified data analytics platform, developed by the original creators of Apache Spark™, designed to accelerate innovation by unifying data science, engineering, and business. The dlt library facilitates this data transfer process, making it easy and efficient. For more details about the Rest API source, please visit the following link: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance with schema inference and evolution and alerts. It uses short declarative code, making maintenance simple. Learn more about it here.
  • Scalability and Efficiency: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. It also uses implicit extraction Directed Acyclic Graphs (DAGs) for efficient API calls for data enrichments or transformations. Find out more here.
  • Data Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. It promotes data consistency, traceability, and control throughout the data processing lifecycle. Read more about it here.
  • Flexible Data Extraction: dlt turns JSON data from any source into a live dataset stored in the destination of your choice. It does this by extracting the JSON data, normalizing it to a schema, and finally loading it to the storage location. Learn more about how dlt works here.
  • Community Support: dlt has a vibrant community where you can ask questions, share how you use the library, or report problems and make feature requests. Join the dlt community 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 Rest API to Databricks. You can run the following commands to create a starting point for loading data from Rest API to Databricks:

# create a new directory
mkdir rest_api_pipeline
cd rest_api_pipeline
# initialize a new pipeline with your source and destination
dlt init rest_api 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:

dlt[databricks]>=0.4.11

You now have the following folder structure in your project:

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

generated secrets.toml​

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

[sources.rest_api]
github_token = "github_token" # please set me up!

[destination.databricks]
dataset_name = "dataset_name" # please set me up!

[destination.databricks.credentials]
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 Rest API 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 rest_api_pipeline.py, as well as a folder rest_api 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:


from typing import Any

import dlt
from rest_api import (
    RESTAPIConfig,
    check_connection,
    rest_api_source,
    rest_api_resources,
)


@dlt.source
def github_source(github_token: str = dlt.secrets.value) -> Any:
    # Create a REST API configuration for the GitHub API
    # Use RESTAPIConfig to get autocompletion and type checking
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.github.com/repos/dlt-hub/dlt/",
            "auth": {
                "type": "bearer",
                "token": github_token,
            },
        },
        # The default configuration for all resources and their endpoints
        "resource_defaults": {
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "params": {
                    "per_page": 100,
                },
            },
        },
        "resources": [
            # This is a simple resource definition,
            # that uses the endpoint path as a resource name:
            # "pulls",
            # Alternatively, you can define the endpoint as a dictionary
            # {
            #     "name": "pulls", # <- Name of the resource
            #     "endpoint": "pulls",  # <- This is the endpoint path
            # }
            # Or use a more detailed configuration:
            {
                "name": "issues",
                "endpoint": {
                    "path": "issues",
                    # Query parameters for the endpoint
                    "params": {
                        "sort": "updated",
                        "direction": "desc",
                        "state": "open",
                        # Define `since` as a special parameter
                        # to incrementally load data from the API.
                        # This works by getting the updated_at value
                        # from the previous response data and using this value
                        # for the `since` query parameter in the next request.
                        "since": {
                            "type": "incremental",
                            "cursor_path": "updated_at",
                            "initial_value": "2024-01-25T11:21:28Z",
                        },
                    },
                },
            },
            # The following is an example of a resource that uses
            # a parent resource (`issues`) to get the `issue_number`
            # and include it in the endpoint path:
            {
                "name": "issue_comments",
                "endpoint": {
                    # The placeholder {issue_number} will be resolved
                    # from the parent resource
                    "path": "issues/{issue_number}/comments",
                    "params": {
                        # The value of `issue_number` will be taken
                        # from the `number` field in the `issues` resource
                        "issue_number": {
                            "type": "resolve",
                            "resource": "issues",
                            "field": "number",
                        }
                    },
                },
                # Include data from `id` field of the parent resource
                # in the child data. The field name in the child data
                # will be called `_issues_id` (_{resource_name}_{field_name})
                "include_from_parent": ["id"],
            },
        ],
    }

    yield from rest_api_resources(config)


def load_github() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rest_api_github",
        destination='databricks',
        dataset_name="rest_api_data",
    )

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


def load_pokemon() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="rest_api_pokemon",
        destination='databricks',
        dataset_name="rest_api_data",
    )

    pokemon_source = rest_api_source(
        {
            "client": {
                "base_url": "https://pokeapi.co/api/v2/",
                # If you leave out the paginator, it will be inferred from the API:
                # paginator: "json_response",
            },
            "resource_defaults": {
                "endpoint": {
                    "params": {
                        "limit": 1000,
                    },
                },
            },
            "resources": [
                "pokemon",
                "berry",
                "location",
            ],
        }
    )

    def check_network_and_authentication() -> None:
        (can_connect, error_msg) = check_connection(
            pokemon_source,
            "not_existing_endpoint",
        )
        if not can_connect:
            pass  # do something with the error message

    check_network_and_authentication()

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


if __name__ == "__main__":
    load_github()
    load_pokemon()

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

python rest_api_pipeline.py

4. Inspecting your load result​

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

dlt pipeline rest_api_github 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 rest_api_github 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 command dlt deploy <script>.py github-action to deploy your pipeline using Github Actions. You can specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your pipeline with Airflow using the command dlt deploy <script>.py airflow-composer. This command creates an Airflow DAG for your pipeline script. Learn more about deploying with Airflow.
  • Deploy with Google Cloud Functions: dlt allows you to deploy your pipeline with Google Cloud Functions. This command creates a Google Cloud Function for your pipeline script. Find out more about deploying with Google Cloud Functions.
  • Other Deployment Options: There are several other ways you can deploy your pipelines using dlt. Visit this page to explore more options.

The running in production section will teach you about:​

  • How to Monitor your pipeline: dlt provides tools for monitoring your pipeline in production, ensuring that everything is running smoothly and efficiently. You can find more information on how to use these tools in the documentation.
  • Set up alerts: To keep you informed about the status of your pipeline, dlt allows you to set up alerts. These alerts can notify you of any issues or changes in your pipeline. Learn how to set up alerts in the documentation.
  • Set up tracing: Tracing is a valuable tool for debugging and understanding your pipeline. dlt provides a built-in tracing feature that can help you understand the flow of data through your pipeline. Find out how to set up tracing in the documentation.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source github_source​

"Rest API Source for GitHub, providing detailed data on issues and related comments."

Resource NameWrite DispositionDescription
issue_commentsmergeContains information about the issue comments including the author, body of the comment, created date, and user details among other data.
issuesmergeContains information about the issues including the assignee details, author, body of the issue, comments, created date, and user details among other data.

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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