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Loading Data from Rest API to AlloyDB with dlt in Python

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:


We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.

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dlt is an open-source Python library that helps you load data from any Rest API into AlloyDB. AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. It combines a Google-built database engine with a cloud-based, multi-node architecture to deliver enterprise-grade performance, reliability, and availability. The rest_api verified source in dlt supports fetching data from any HTTP Rest API and loading it into AlloyDB. For more details, visit this link.

dlt Key Features

  • Automated maintenance: dlt offers automated schema inference, evolution, and alerts, simplifying maintenance. Learn more
  • Scalability: Efficiently process large datasets using iterators, chunking, and parallelization techniques. Read about scalability
  • Data type support: Handle various data types like text, double, bool, timestamp, date, and more with precision and scale. Explore data types
  • Key pair authentication: Securely authenticate with Snowflake using base64-encoded private keys and optional passphrase. Check key pair authentication
  • Implicit extraction DAGs: Automatically manage dependencies between data sources and transformations for efficient extraction. Understand extraction DAGs

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 AlloyDB:

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

# 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 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # 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

generated secrets.toml

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

github_token = "github_token" # please set me up!

dataset_name = "dataset_name" # please set me up!

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 Rest API source in our docs.
  • Read more about setting up the AlloyDB 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 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 (

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": "",
"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(

load_info =

def load_pokemon() -> None:
pipeline = dlt.pipeline(

pokemon_source = rest_api_source(
"client": {
"base_url": "",
# If you leave out the paginator, it will be inferred from the API:
# paginator: "json_response",
"resource_defaults": {
"endpoint": {
"params": {
"limit": 1000,
"resources": [

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


load_info =

if __name__ == "__main__":

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 rest_api_github info

You can also use streamlit to inspect the contents of your AlloyDB 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: Learn how to deploy a pipeline using GitHub Actions for CI/CD. GitHub Actions
  • Deploy with Airflow and Google Composer: Follow this guide to deploy a pipeline with Airflow and Google Composer. Airflow
  • Deploy with Google Cloud Functions: Discover how to deploy a pipeline using Google Cloud Functions. Google Cloud Functions
  • Other Deployment Methods: Explore other methods to deploy your pipeline, including different cloud providers and environments. and others...

The running in production section will teach you about:

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth and accurate data processing. How to Monitor your pipeline
  • Set up alerts: Configure alerts to stay informed about the status and performance of your pipeline, allowing you to quickly respond to any issues. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the execution of your pipeline, including timing information and configuration details. And 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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