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Python Data Loading from rest_api to bigquery using dlt Library

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 page provides technical documentation on how to use the dlt Python library to load data from a Rest API into BigQuery. BigQuery is a serverless, cost-effective enterprise data warehouse that operates across multiple clouds and scales according to your data needs. The rest_api verified source within the dlt library supports the extraction of data from any http Rest API for loading into BigQuery. For more detailed information on this source, please refer to the official dlt documentation at https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api.

dlt Key Features​

  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more
  • Run it where Python runs: dlt can run on Airflow, serverless functions, notebooks. No external APIs, backends or containers, it scales on micro and large infra alike. Learn more
  • User-friendly, declarative interface: dlt removes knowledge obstacles for beginners while empowering senior professionals. Learn more
  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go. Learn more
  • Flexible and powerful: dlt can automatically turn JSON returned by any source into a live dataset stored in the destination of your choice. 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. 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 BigQuery:

pip install "dlt[bigquery]"

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 BigQuery. You can run the following commands to create a starting point for loading data from Rest API to BigQuery:

# 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 bigquery
# 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[bigquery]>=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.bigquery]
dataset_name = "dataset_name" # please set me up!
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 BigQuery 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='bigquery',
        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='bigquery',
        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 BigQuery 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 allows you to deploy your pipelines using Github Actions. This CI/CD runner is free and easy to use, making it a great choice for deployment.
  • Deploy with Airflow and Google Composer: You can also deploy your dlt pipelines with Airflow and Google Composer. This guide walks you through the process of deploying a pipeline with this managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: If you prefer using Google Cloud Functions, dlt has you covered. Follow this guide to learn how to deploy a pipeline with Google Cloud Functions.
  • Other Deployment Methods: dlt supports a variety of other deployment methods. Visit the deployment walkthroughs page to learn more.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep track of your pipeline's progress and performance. This helps in identifying any issues that may arise during the operation of your pipeline. For more information, visit How to Monitor your pipeline.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues in your pipeline. This ensures that you are always aware of the status of your pipeline and can take immediate action when necessary. For more details, check out Set up alerts.
  • Set Up Tracing: Tracing allows you to track the execution of your pipeline and understand its performance. dlt provides tools for setting up tracing, making it easier for you to optimize your pipeline. Learn more about it at Set up tracing.

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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