Loading Data from Capsule CRM
to Dremio
with dlt
in Python
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Loading data from Capsule CRM
to Dremio
using the open-source python library dlt
can greatly enhance your data management capabilities. Capsule CRM
is a user-friendly customer relationship management platform that helps businesses manage customer interactions and sales pipelines effectively. It includes features such as contact management, task tracking, sales analytics, and workflow automation. On the other hand, Dremio
is a data lakehouse solution that provides flexibility, scalability, and performance to bring users closer to their data. By leveraging dlt
, you can streamline the process of transferring data from Capsule CRM
to Dremio
, ensuring that your sales and customer interaction data is readily available for analysis and decision-making. For more details on Capsule CRM
, visit here.
dlt
Key Features
- Pipeline Metadata:
dlt
pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, which consist of a timestamp and pipeline name. Load IDs enable incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more about lineage. - 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. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more. - 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. Learn more. - dbt Integration:
dlt
seamlessly integrates with dbt, a powerful framework for transforming data. It enables you to structure your transformations into DAGs, providing cross-database compatibility and various features such as templating, backfills, testing, and troubleshooting. See example. - Using Pandas for Transformations: You can fetch query results as Pandas data frames and perform transformations using Pandas functionalities. This allows you to leverage the powerful data manipulation capabilities of Pandas within your
dlt
pipelines. 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 capsule_crm --url https://raw.githubusercontent.com/dlt-hub/openapi-specs/main/open_api_specs/Business/capsule_crm.yaml --global-limit 2
cd capsule_crm_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:
capsule_crm_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── rest_api/ # The rest api verified source
│ └── ...
├── capsule_crm/
│ └── __init__.py # TODO: possibly tweak this file
├── capsule_crm_pipeline.py # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)
1.1. Tweak capsule_crm/__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 capsule_crm source will look like this:
Click to view full file (275 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="capsule_crm_source", max_table_nesting=2)
def capsule_crm_source(
token: str = dlt.secrets.value,
base_url: str = dlt.config.value,
) -> List[DltResource]:
# source configuration
source_config: RESTAPIConfig = {
"client": {
"base_url": base_url,
"auth": {
"type": "bearer",
"token": token,
},
"paginator": {
"type":
"page_number",
"page_param":
"page",
"total_path":
"",
"maximum_page":
20,
},
},
"resources":
[
# https://developer.capsulecrm.com/v2/operations/Case#listCases
{
"name": "list_cases",
"table_name": "case",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "kases",
"path": "/api/v2/kases",
"params": {
# the parameters below can optionally be configured
# "since": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Case#searchCases
{
"name": "search_cases",
"table_name": "case",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "kases",
"path": "/api/v2/kases/search",
"params": {
# the parameters below can optionally be configured
# "q": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Case#showCase
{
"name": "show_case",
"table_name": "case",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "kase",
"path": "/api/v2/kases/{caseId}",
"params": {
"caseId": {
"type": "resolve",
"resource": "list_cases",
"field": "id",
},
# the parameters below can optionally be configured
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Case#listCasesByParty
{
"name": "list_cases_by_party",
"table_name": "case",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "kases",
"path": "/api/v2/parties/{partyId}/kases",
"params": {
"partyId": {
"type": "resolve",
"resource": "list_parties",
"field": "id",
},
# the parameters below can optionally be configured
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Opportunity#listOpportunities
{
"name": "list_opportunities",
"table_name": "opportunity",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "opportunities",
"path": "/api/v2/opportunities",
"params": {
# the parameters below can optionally be configured
# "since": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Opportunity#searchOpportunities
{
"name": "search_opportunities",
"table_name": "opportunity",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "opportunities",
"path": "/api/v2/opportunities/search",
"params": {
# the parameters below can optionally be configured
# "q": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Opportunity#showOpportunity
{
"name": "show_opportunity",
"table_name": "opportunity",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "opportunity",
"path": "/api/v2/opportunities/{opportunityId}",
"params": {
"opportunityId": {
"type": "resolve",
"resource": "list_opportunities",
"field": "id",
},
# the parameters below can optionally be configured
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Opportunity#listOpportunitiesByParty
{
"name": "list_opportunities_by_party",
"table_name": "opportunity",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "opportunities",
"path": "/api/v2/parties/{partyId}/opportunities",
"params": {
"partyId": {
"type": "resolve",
"resource": "list_parties",
"field": "id",
},
# the parameters below can optionally be configured
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Party#listParties
{
"name": "list_parties",
"table_name": "party",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "parties",
"path": "/api/v2/parties",
"params": {
# the parameters below can optionally be configured
# "since": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Party#searchParties
{
"name": "search_parties",
"table_name": "party",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "parties",
"path": "/api/v2/parties/search",
"params": {
# the parameters below can optionally be configured
# "q": "OPTIONAL_CONFIG",
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Party#showParty
{
"name": "show_party",
"table_name": "party",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "party",
"path": "/api/v2/parties/{partyId}",
"params": {
"partyId": {
"type": "resolve",
"resource": "list_parties",
"field": "id",
},
# the parameters below can optionally be configured
# "embed": "OPTIONAL_CONFIG",
},
}
},
# https://developer.capsulecrm.com/v2/operations/Task#listTasks
{
"name": "list_tasks",
"table_name": "task",
"primary_key": "id",
"write_disposition": "merge",
"endpoint": {
"data_selector": "tasks",
"path": "/api/v2/tasks",
"params": {
# the parameters below can optionally be configured
# "perPage": "OPTIONAL_CONFIG",
# "embed": "OPTIONAL_CONFIG",
# "status": "OPTIONAL_CONFIG",
},
}
},
]
}
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.capsule_crm]
# Base URL for the API
base_url = "https://api.capsulecrm.com"
generated secrets.toml
[sources.capsule_crm]
# secrets for your capsule_crm source
token = "FILL ME OUT" # TODO: fill in your credentials
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 capsule_crm_pipeline.py
to dremio 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 capsule_crm_pipeline.py
, as well as a folder capsule_crm
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 capsule_crm import capsule_crm_source
if __name__ == "__main__":
pipeline = dlt.pipeline(
pipeline_name="capsule_crm_pipeline",
destination='duckdb',
dataset_name="capsule_crm_data",
progress="log",
export_schema_path="schemas/export"
)
source = capsule_crm_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 capsule_crm_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline capsule_crm_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 capsule_crm_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 set up and use GitHub Actions to deploy your
dlt
pipelines efficiently. Follow the guide here. - Deploy with Airflow: Use Airflow and Google Composer for managing and scheduling your
dlt
pipelines. Detailed instructions can be found here. - Deploy with Google Cloud Functions: Explore deploying your
dlt
pipelines using Google Cloud Functions for a serverless execution environment. Check the guide here. - Explore other deployment options: Discover various other methods and platforms for deploying your
dlt
pipelines by visiting this page.
The running in production section will teach you about:
- How to Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline to ensure it runs smoothly and efficiently. How to Monitor your pipeline - Set up alerts: Configure alerts to stay informed about the status and health of your
dlt
pipeline. Set up alerts - Set up tracing: Implement tracing to get detailed insights into your pipeline's execution, helping you diagnose and resolve issues quickly. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source Capsule CRM
Capsule CRM: Manage contacts, tasks, sales opportunities, and customer cases.
Resource Name | Write Disposition | Description |
---|---|---|
party | append | Refers to contacts or organizations that interact with the business |
task | append | Used to track and manage activities and to-dos within the CRM |
opportunity | append | Represents potential sales or deals that are tracked through various stages |
case | append | Used for managing customer support issues or service requests |
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