Loading Data from `ClickHouse Cloud` to `Dremio` Using `dlt` in Python

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Loading data from ClickHouse Cloud to Dremio using the open-source Python library dlt is a streamlined process designed for efficiency and scalability. ClickHouse Cloud is a high-performance, scalable cloud-based data warehousing solution tailored for real-time analytics. It allows businesses to run complex queries on large datasets with remarkable speed and efficiency. On the other hand, Dremio is a versatile data lakehouse solution that supports users at all stages of their data journey, offering flexibility, scalability, and performance. By leveraging dlt, users can seamlessly transfer data between ClickHouse Cloud and Dremio, ensuring robust data management and analytics capabilities. For more information on ClickHouse Cloud, visit ClickHouse Cloud.

`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: On Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
User-friendly interface: Declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
Scalability: Offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Learn more
Governance support: Provides governance capabilities through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn 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 clickhouse_cloud --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/click_house_cloud.yaml --global-limit 2
cd clickhouse_cloud_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:

clickhouse_cloud_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── rest_api/                # The rest api verified source
│   └── ...
├── clickhouse_cloud/                
│   └── __init__.py          # TODO: possibly tweak this file
├── clickhouse_cloud_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

1.1. Tweak clickhouse_cloud/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 clickhouse_cloud source will look like this:

Click to view full file (229 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="clickhouse_cloud_source", max_table_nesting=2)
def clickhouse_cloud_source(
        base_url: str = dlt.config.value,
) -> List[DltResource]:

    # source configuration
    source_config: RESTAPIConfig = {
        "client": {
            "base_url": base_url,
        },
        "resources": 
        [
        # Returns a list of all organization activities.
        {
            "name": "organization_id_activities",
            "table_name": "activity",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/activities",
                "paginator": "auto",
            }
        },
        # Returns a single organization activity by ID.
        {
            "name": "organization_id_activities_activity_id",
            "table_name": "activity_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/activities/:activityId",
                "paginator": "auto",
            }
        },
        # Returns a list of all keys in the organization.
        {
            "name": "organization_id_keys",
            "table_name": "api_key",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/keys",
                "paginator": "auto",
            }
        },
        # Returns a list of all backups for the service. The most recent backups comes first in the list.
        {
            "name": "organization_id_services_service_id_backups",
            "table_name": "backup",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/services/:serviceId/backups",
                "paginator": "auto",
            }
        },
        # Returns a single backup info.
        {
            "name": "organization_id_services_service_id_backups_backup_id",
            "table_name": "backup_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/services/:serviceId/backups/:backupId",
                "paginator": "auto",
            }
        },
        # Returns list of all organization invitations.
        {
            "name": "organization_id_invitations",
            "table_name": "invitation",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/invitations",
                "paginator": "auto",
            }
        },
        # Returns details for a single organization invitation.
        {
            "name": "organization_id_invitations_invitation_id",
            "table_name": "invitation_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/invitations/:invitationId",
                "paginator": "auto",
            }
        },
        # Returns a single key details.
        {
            "name": "organization_id_keys_key_id",
            "table_name": "key_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/keys/:keyId",
                "paginator": "auto",
            }
        },
        # Returns a list of all members in the organization.
        {
            "name": "organization_id_members",
            "table_name": "member",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/members",
                "paginator": "auto",
            }
        },
        # Returns a list with a single organization associated with the API key in the request.
        {
            "name": "",
            "table_name": "organization",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations",
                "paginator": "auto",
            }
        },
        # Returns details of a single organization. In order to get the details, the auth key must belong to the organization.
        {
            "name": "organization_id",
            "table_name": "organization_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId",
                "paginator": "auto",
            }
        },
        # Information required to set up a private endpoint
        {
            "name": "organization_id_services_service_id_private_endpoint_config",
            "table_name": "private_endpoint_config",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/services/:serviceId/privateEndpointConfig",
                "paginator": "auto",
            }
        },
        # Information required to set up a private endpoint
        {
            "name": "organization_id_private_endpoint_config",
            "table_name": "private_endpoint_config",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/privateEndpointConfig",
                "params": {
                    "Cloud provider identifier": "FILL_ME_IN", # TODO: fill in required query parameter
                    "Cloud provider region": "FILL_ME_IN", # TODO: fill in required query parameter

                },
                "paginator": "auto",
            }
        },
        # Returns prometheus metrics for a service. Please contact support to enable this feature.
        {
            "name": "organization_id_services_service_id_prometheus",
            "table_name": "prometheu",
            "endpoint": {
                "path": "/v1/organizations/:organizationId/services/:serviceId/prometheus",
                "paginator": "auto",
            }
        },
        # Returns a list of all services in the organization.
        {
            "name": "organization_id_services",
            "table_name": "service",
            "primary_key": "id",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "result",
                "path": "/v1/organizations/:organizationId/services",
                "paginator": "auto",
            }
        },
        # Returns a service that belongs to the organization
        {
            "name": "organization_id_services_service_id",
            "table_name": "service_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/services/:serviceId",
                "paginator": "auto",
            }
        },
        # Returns a single organization member details.
        {
            "name": "organization_id_members_user_id",
            "table_name": "user_id",
            "primary_key": "requestId",
            "write_disposition": "merge",
            "endpoint": {
                "data_selector": "$",
                "path": "/v1/organizations/:organizationId/members/:userId",
                "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.clickhouse_cloud]
# Base URL for the API
base_url = "https://api.clickhouse.cloud"

generated secrets.toml

[sources.clickhouse_cloud]
# secrets for your clickhouse_cloud 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 clickhouse_cloud_pipeline.py to dremio 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 Dremio 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 clickhouse_cloud_pipeline.py, as well as a folder clickhouse_cloud 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 clickhouse_cloud import clickhouse_cloud_source


if __name__ == "__main__":
    pipeline = dlt.pipeline(
        pipeline_name="clickhouse_cloud_pipeline",
        destination='duckdb',
        dataset_name="clickhouse_cloud_data",
        progress="log",
        export_schema_path="schemas/export"
    )
    source = clickhouse_cloud_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 clickhouse_cloud_pipeline.py

4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline clickhouse_cloud_pipeline info

You can also use streamlit to inspect the contents of your Dremio destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline clickhouse_cloud_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: Use Google Composer, a managed Airflow environment provided by Google, to deploy your dlt pipeline. Detailed instructions can be found here.
Deploy with Google Cloud Functions: Discover how to deploy your dlt pipeline using Google Cloud Functions for a serverless deployment solution. Follow the steps here.
Explore Other Deployment Options: Check out additional methods and detailed guides for deploying dlt pipelines here.

The running in production section will teach you about:

How to Monitor your pipeline: Learn how to effectively monitor your dlt pipelines to ensure smooth and efficient operations. Visit How to Monitor your pipeline.
Set up alerts: Set up alerts to get notified of any issues or important events in your dlt pipelines. Check out Set up alerts.
And set up tracing: Implement tracing to keep track of your pipeline's performance and debug any potential issues. See And set up tracing.

Available Sources and Resources

For this verified source the following sources and resources are available

Source `ClickHouse Cloud`

Streams various organizational, user activity, and configuration data from ClickHouse Cloud.

Resource Name	Write Disposition	Description
`activity`	`append`	Logs and tracks user activities within the ClickHouse Cloud platform.
`api_key`	`append`	Stores API keys used for authenticating and authorizing API requests.
`invitation_id`	`append`	Unique identifiers for invitations sent to users for accessing the platform.
`organization_id`	`append`	Unique identifiers for different organizations using the ClickHouse Cloud service.
`prometheu`	`append`	Stores Prometheus monitoring data for performance and health metrics.
`invitation`	`append`	Contains details of invitations sent to users for joining the platform.
`activity_id`	`append`	Unique identifiers for specific activities logged within the platform.
`member`	`append`	Information about members of various organizations within ClickHouse Cloud.
`private_endpoint_config`	`append`	Configuration settings for private endpoints used to access ClickHouse Cloud securely.
`service`	`append`	Details about various services provided by ClickHouse Cloud.
`service_id`	`append`	Unique identifiers for different services within the platform.
`backup`	`append`	Information about backups created for data stored in ClickHouse Cloud.
`user_id`	`append`	Unique identifiers for users accessing the ClickHouse Cloud platform.
`key_id`	`append`	Unique identifiers for API keys used within the platform.
`backup_id`	`append`	Unique identifiers for backups created within the platform.
`organization`	`append`	Details about organizations using ClickHouse Cloud, including names and contact information.

Loading Data from `ClickHouse Cloud` to `Dremio` Using `dlt` in Python

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt and dlt-init-openapi

1.1. Tweak clickhouse_cloud/init.py

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `ClickHouse Cloud`

Additional pipeline guides

DHelp

Ask a question

Loading Data from ClickHouse Cloud to Dremio Using dlt in Python

dlt Key Features​

Getting started with your pipeline locally​

0. Prerequisites​

1. Install dlt and dlt-init-openapi​

1.1. Tweak clickhouse_cloud/__init__.py​

2. Configuring your source and destination credentials​

generated config.toml​

generated secrets.toml​

2.1. Adjust the generated code to your usecase​

3. Running your pipeline for the first time​

4. Inspecting your load result​

5. Next steps to get your pipeline running in production​

The Deploy section will show you how to deploy your pipeline to​

The running in production section will teach you about:​

Available Sources and Resources​

Source ClickHouse Cloud​

Additional pipeline guides​

DHelp

Ask a question

Loading Data from `ClickHouse Cloud` to `Dremio` Using `dlt` in Python

`dlt` Key Features

Getting started with your pipeline locally

0. Prerequisites

1. Install dlt and dlt-init-openapi

1.1. Tweak clickhouse_cloud/init.py

2. Configuring your source and destination credentials

generated config.toml

generated secrets.toml

2.1. Adjust the generated code to your usecase

3. Running your pipeline for the first time

4. Inspecting your load result

5. Next steps to get your pipeline running in production

The Deploy section will show you how to deploy your pipeline to

The running in production section will teach you about:

Available Sources and Resources

Source `ClickHouse Cloud`

Additional pipeline guides