Loading Data from Looker to YugabyteDB using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to YugabyteDB. You can get the connection string for your YugabyteDB database as described in the YugabyteDB Docs.

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Looker is a modern data platform that enables businesses to explore, analyze, and share real-time business analytics easily. It provides powerful tools for data visualization, dashboards, and interactive reports. Looker helps businesses make data-driven decisions by connecting directly to their databases and allowing users to create custom queries and visualizations without needing extensive SQL knowledge. YugabyteDB is a distributed PostgreSQL database designed for modern applications. It offers built-in resilience, seamless scalability, and flexible geo-distribution using PostgreSQL-compatible and Cassandra-inspired APIs. In this documentation, we will guide you through loading data from Looker to YugabyteDB using the open-source Python library dlt. This integration allows efficient data transfer and management, leveraging the strengths of both platforms. For more information on Looker, visit here.

dlt Key Features

• Scalability via iterators, chunking, and parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This approach allows for efficient processing of large datasets by breaking them down into manageable chunks. Read more
• Implicit extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. A DAG represents a directed graph without cycles, where each node represents a data source or transformation step. Read more
• Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more
• Performance Optimization: Yield pages instead of rows when producing data. This makes some processes more effective by lowering the necessary function calls. Read more
• Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more

Getting started with your pipeline locally

OpenAPI Source Generator dlt-init-openapi

This walkthrough makes use of the dlt-init-openapi generator cli tool. You can read more about it here. The code generated by this tool uses the dlt rest_api verified source, docs for this are here.

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

info

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.

• If you know your API needs authentication, but none was detected, you can learn more about adding authentication to the rest_api here.
• OAuth detection currently is not supported, but you can supply your own authentication mechanism as outlined here.

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

Further help setting up your source and destinations

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 postgres and supply the credentials as outlined in the destination doc linked below.

• Read more about setting up the rest_api source in our docs.
• Read more about setting up the YugabyteDB 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 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 YugabyteDB 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, a CI/CD runner that you can use for free. Follow the guide here.
• Deploy with Airflow and Google Composer: This guide shows you how to deploy your pipeline with Airflow, using Google Composer, a managed Airflow environment provided by Google. Check it out here.
• Deploy with Google Cloud Functions: Deploy your dlt pipeline using Google Cloud Functions, a serverless execution environment. Detailed steps can be found here.
• Explore other deployment options: Discover various other methods to deploy your dlt pipeline, including different cloud services and environments. Learn more here.

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 error-free operations. How to Monitor your pipeline
• Set up alerts: Set up alerts for your dlt pipeline to get notified about important events and errors. Set up alerts
• Set up tracing: Implement tracing in your dlt pipeline to track the flow and performance of your data. 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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