Loading Data from Looker
to Snowflake
Using dlt
in Python
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Looker
is a modern data platform that allows businesses to explore, analyze, and share real-time business analytics with ease. It offers powerful tools for data visualization, dashboards, and interactive reports, enabling data-driven decisions without requiring extensive SQL knowledge. Snowflake
is a cloud-based data warehousing platform designed for the storage, processing, and analysis of large volumes of data. This documentation explains how to load data from Looker
to Snowflake
using the open-source Python library dlt
. For more information about Looker
, visit Looker.
dlt
Key Features
- Scalability via iterators, chunking, and parallelization:
dlt
offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Read more - Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more - Schema Enforcement and Curation:
dlt
empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more - Schema evolution:
dlt
enables proactive governance by alerting users to schema changes, allowing necessary actions like reviewing and validating changes. Read more - Implicit extraction DAGs:
dlt
incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and transformations automatically. Read more
Getting started with your pipeline locally
dlt-init-openapi
0. Prerequisites
dlt
and dlt-init-openapi
requires Python 3.9 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 and dlt-init-openapi
First you need to install the dlt-init-openapi
cli tool.
pip install dlt-init-openapi
The dlt-init-openapi
cli is a powerful generator which you can use to turn any OpenAPI spec into a dlt
source to ingest data from that api. The quality of the generator source is dependent on how well the API is designed and how accurate the OpenAPI spec you are using is. You may need to make tweaks to the generated code, you can learn more about this here.
# generate pipeline
# NOTE: add_limit adds a global limit, you can remove this later
# NOTE: you will need to select which endpoints to render, you
# can just hit Enter and all will be rendered.
dlt-init-openapi looker --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/looker.yaml --global-limit 2
cd looker_pipeline
# install generated requirements
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>=0.4.12
You now have the following folder structure in your project:
looker_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── looker/
│ └── __init__.py # TODO: possibly tweak this file
├── looker_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak looker/__init__.py
This file contains the generated configuration of your rest_api. You can continue with the next steps and leave it as is, but you might want to come back here and make adjustments if you need your rest_api
source set up in a different way. The generated file for the looker source will look like this:
Click to view full file (169 lines)
from typing import List
import dlt
from dlt.extract.source import DltResource
from rest_api import rest_api_source
from rest_api.typing import RESTAPIConfig
@dlt.source(name="looker_source", max_table_nesting=2)
def looker_source(
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
},
"resources":
[
# Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.
{
"name": "resourceget_iam_policy",
"table_name": "audit_config",
"endpoint": {
"data_selector": "auditConfigs",
"path": "/v1/{resource}:getIamPolicy",
"params": {
# the parameters below can optionally be configured
# "$.xgafv": "OPTIONAL_CONFIG",
# "access_token": "OPTIONAL_CONFIG",
# "alt": "OPTIONAL_CONFIG",
# "callback": "OPTIONAL_CONFIG",
# "fields": "OPTIONAL_CONFIG",
# "key": "OPTIONAL_CONFIG",
# "oauth_token": "OPTIONAL_CONFIG",
# "prettyPrint": "OPTIONAL_CONFIG",
# "quotaUser": "OPTIONAL_CONFIG",
# "upload_protocol": "OPTIONAL_CONFIG",
# "uploadType": "OPTIONAL_CONFIG",
# "options.requestedPolicyVersion": "OPTIONAL_CONFIG",
},
"paginator": "auto",
}
},
# Lists Instances in a given project and location.
{
"name": "instances",
"table_name": "instance",
"endpoint": {
"data_selector": "instances",
"path": "/v1/{parent}/instances",
"params": {
"parent": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "$.xgafv": "OPTIONAL_CONFIG",
# "access_token": "OPTIONAL_CONFIG",
# "alt": "OPTIONAL_CONFIG",
# "callback": "OPTIONAL_CONFIG",
# "fields": "OPTIONAL_CONFIG",
# "key": "OPTIONAL_CONFIG",
# "oauth_token": "OPTIONAL_CONFIG",
# "prettyPrint": "OPTIONAL_CONFIG",
# "quotaUser": "OPTIONAL_CONFIG",
# "upload_protocol": "OPTIONAL_CONFIG",
# "uploadType": "OPTIONAL_CONFIG",
# "pageSize": "OPTIONAL_CONFIG",
# "pageToken": "OPTIONAL_CONFIG",
},
"paginator": "auto",
}
},
# Lists information about the supported locations for this service.
{
"name": "locations",
"table_name": "location",
"primary_key": "name",
"write_disposition": "merge",
"endpoint": {
"data_selector": "locations",
"path": "/v1/{name}/locations",
"params": {
"name": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "$.xgafv": "OPTIONAL_CONFIG",
# "access_token": "OPTIONAL_CONFIG",
# "alt": "OPTIONAL_CONFIG",
# "callback": "OPTIONAL_CONFIG",
# "fields": "OPTIONAL_CONFIG",
# "key": "OPTIONAL_CONFIG",
# "oauth_token": "OPTIONAL_CONFIG",
# "prettyPrint": "OPTIONAL_CONFIG",
# "quotaUser": "OPTIONAL_CONFIG",
# "upload_protocol": "OPTIONAL_CONFIG",
# "uploadType": "OPTIONAL_CONFIG",
# "filter": "OPTIONAL_CONFIG",
# "pageSize": "OPTIONAL_CONFIG",
# "pageToken": "OPTIONAL_CONFIG",
},
"paginator": "auto",
}
},
# Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.
{
"name": "",
"table_name": "operation",
"primary_key": "name",
"write_disposition": "merge",
"endpoint": {
"data_selector": "$",
"path": "/v1/{name}",
"params": {
"name": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "$.xgafv": "OPTIONAL_CONFIG",
# "access_token": "OPTIONAL_CONFIG",
# "alt": "OPTIONAL_CONFIG",
# "callback": "OPTIONAL_CONFIG",
# "fields": "OPTIONAL_CONFIG",
# "key": "OPTIONAL_CONFIG",
# "oauth_token": "OPTIONAL_CONFIG",
# "prettyPrint": "OPTIONAL_CONFIG",
# "quotaUser": "OPTIONAL_CONFIG",
# "upload_protocol": "OPTIONAL_CONFIG",
# "uploadType": "OPTIONAL_CONFIG",
},
"paginator": "auto",
}
},
# Lists operations that match the specified filter in the request. If the server doesn't support this method, it returns `UNIMPLEMENTED`.
{
"name": "operations",
"table_name": "operation",
"primary_key": "name",
"write_disposition": "merge",
"endpoint": {
"data_selector": "operations",
"path": "/v1/{name}/operations",
"params": {
"name": "FILL_ME_IN", # TODO: fill in required path parameter
# the parameters below can optionally be configured
# "$.xgafv": "OPTIONAL_CONFIG",
# "access_token": "OPTIONAL_CONFIG",
# "alt": "OPTIONAL_CONFIG",
# "callback": "OPTIONAL_CONFIG",
# "fields": "OPTIONAL_CONFIG",
# "key": "OPTIONAL_CONFIG",
# "oauth_token": "OPTIONAL_CONFIG",
# "prettyPrint": "OPTIONAL_CONFIG",
# "quotaUser": "OPTIONAL_CONFIG",
# "upload_protocol": "OPTIONAL_CONFIG",
# "uploadType": "OPTIONAL_CONFIG",
# "filter": "OPTIONAL_CONFIG",
# "pageSize": "OPTIONAL_CONFIG",
# "pageToken": "OPTIONAL_CONFIG",
},
"paginator": "auto",
}
},
]
}
return rest_api_source(source_config)
2. Configuring your source and destination credentials
dlt-init-openapi
will try to detect which authentication mechanism (if any) is used by the API in question and add a placeholder in your secrets.toml
.
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
[runtime]
log_level="INFO"
[sources.looker]
# Base URL for the API
base_url = "https://looker.googleapis.com/"
generated secrets.toml
[sources.looker]
# secrets for your looker source
# example_api_key = "example value"
2.1. Adjust the generated code to your usecase
At this time, the dlt-init-openapi
cli tool will always create pipelines that load to a local duckdb
instance. Switching to a different destination is trivial, all you need to do is change the destination
parameter in looker_pipeline.py
to snowflake and supply the credentials as outlined in the destination doc linked below.
3. Running your pipeline for the first time
The dlt
cli has also created a main pipeline script for you at looker_pipeline.py
, as well as a folder looker
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:
import dlt
from looker import looker_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="looker_pipeline",
destination='duckdb',
dataset_name="looker_data",
progress="log",
export_schema_path="schemas/export"
)
source = looker_source()
info = pipeline.run(source)
print(info)
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python looker_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline looker_pipeline 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 looker_pipeline 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 your
dlt
pipeline using GitHub Actions for CI/CD. Follow the steps in the guide. - Deploy with Airflow: Use Google Composer to deploy your
dlt
pipeline with Airflow. Detailed instructions can be found here. - Deploy with Google Cloud Functions: Discover how to deploy your
dlt
pipeline using Google Cloud Functions by following this tutorial. - Explore more deployment options: For additional methods to deploy your
dlt
pipeline, check out the full list of walkthroughs available here.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to efficiently monitor your
dlt
pipeline in production to ensure smooth and reliable operation. For more information, visit How to Monitor your pipeline. - Set up alerts: Setting up alerts helps you stay informed about any issues or anomalies in your
dlt
pipeline. Follow the detailed guide at Set up alerts. - Set up tracing: Tracing enables you to track the execution flow and performance of your pipeline, making it easier to debug and optimize. Check out the guide at And set up tracing.
Available Sources and Resources
For this verified source the following sources and resources are available
Source Looker
Streams Looker data including configurations, operations, and audit logs.
Resource Name | Write Disposition | Description |
---|---|---|
instance | append | Represents an instance in Looker, containing configuration details and status of the instance. |
audit_config | append | Contains audit configurations, which track user activities and changes within Looker. |
location | append | Details regarding the geographical location and associated settings of the Looker instance. |
operation | append | Represents various operations performed within Looker, including their status and metadata. |
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