Loading Data from rest_api
to snowflake
using Python dlt
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:
- The rest_api docs to learn how to configure this verified source
- The OpenAPI generator docs to learn how to automatically configure a dlt rest_api source from an OpenAPI spec
- Our cool google colab example demonstrating the generator and the rest_api source
Join our Slack community or book a call with our support engineer Violetta.
This page provides technical documentation on how to utilize dlt
, an open-source Python library, to load data from a Rest API
into Snowflake
. Snowflake
is a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. The rest_api
verified source in dlt
enables the extraction of data from any HTTP Rest API
, facilitating its subsequent loading into Snowflake
. For more detailed information about the rest_api
source, visit the official dlt
documentation at https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api.
dlt
Key Features
- Authentication Types: Snowflake destination accepts three authentication types - password authentication, key pair authentication, and external authentication. Each of these methods is explained in detail with examples. Read More
- Governance Support:
dlt
pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Read More - Scalability and Fine-tuning:
dlt
offers several mechanisms and configuration options to scale up and fine-tune pipelines. It supports running extraction, normalization, and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and fine-tuning the memory buffers, intermediary file sizes, and compression options. Read More - Data Extraction:
dlt
makes data extraction simple by decorating your data-producing functions with loading or incremental extraction metadata. It leverages scalability through iterators, chunking, parallelization, and the utilization of implicit extraction Directed Acyclic Graphs (DAGs) for efficient API calls for data enrichments or transformations. Read More - How
dlt
Works:dlt
turns 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. Read 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 Snowflake
:
pip install "dlt[snowflake]"
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 Snowflake
. You can run the following commands to create a starting point for loading data from Rest API
to Snowflake
:
# 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 snowflake
# 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[snowflake]>=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.snowflake]
dataset_name = "dataset_name" # please set me up!
[destination.snowflake.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!
2.1. Adjust the generated code to your usecase
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='snowflake',
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='snowflake',
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 Snowflake
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
can be deployed using Github Actions. Github Actions is a CI/CD runner that can be used for free. - Deploy with Airflow: You can also deploy
dlt
using Airflow. Google Composer, a managed Airflow environment provided by Google, can be used for this purpose. - Deploy with Google Cloud Functions: Google Cloud Functions is another method to deploy
dlt
. This serverless execution environment runs your code in response to events and automatically manages the resources for you. - Other Deployment Methods: There are also other methods to deploy
dlt
, depending on your specific needs and use case.
The running in production section will teach you about:
- Monitor Your Pipeline:
dlt
provides robust tools to monitor your pipeline's performance and status. It allows you to inspect and save load info, trace runtime, and alert on schema changes. Learn more about How to Monitor your pipeline. - Set Up Alerts: With
dlt
, you can set up alerts to notify you about any changes or issues in your pipeline. This feature ensures that you are always aware of your pipeline's status and can promptly address any problems. Learn how to Set up alerts. - Set Up Tracing:
dlt
also allows you to set up tracing to keep track of your pipeline's operations. With tracing, you can easily identify and debug any issues in your pipeline. Learn more about Setting 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 Name | Write Disposition | Description |
---|---|---|
issue_comments | merge | Contains information about the issue comments including the author, body of the comment, created date, and user details among other data. |
issues | merge | Contains information about the issues including the assignee details, author, body of the issue, comments, created date, and user details among other data. |
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