Loading Data from Pipedrive
to AlloyDB
with dlt
in Python
We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.
Join our Slack community or book a call with our support engineer Violetta.
This page provides technical documentation on loading data from Pipedrive
to AlloyDB
using the open-source Python library dlt
. Pipedrive
is a business messaging app that connects people to the information they need. AlloyDB
for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. It combines a Google-built database engine with a cloud-based, multi-node architecture to offer enterprise-grade performance, reliability, and availability. This guide will walk you through the steps to integrate Pipedrive
with AlloyDB
using dlt
. For more information on Pipedrive
, visit Pipedrive.
dlt
Key Features
- **Automated maintenance**: With schema inference, evolution, and alerts, maintenance becomes simple, allowing you to focus on leveraging data. [Learn more](https://dlthub.com/docs/intro)
- **Scalability**: `dlt` offers scalable data extraction using iterators, chunking, and parallelization techniques, making it efficient for large datasets. [Read about extracting data](https://dlthub.com/docs/build-a-pipeline-tutorial)
- **Pipeline Governance**: `dlt` pipelines provide governance through pipeline metadata, schema enforcement, and schema change alerts to ensure data consistency and quality. [Explore governance support](https://dlthub.com/docs/build-a-pipeline-tutorial)
- **User-friendly interface**: `dlt` features a declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. [Get started with `dlt`](https://dlthub.com/docs/getting-started)
- **Community and Support**: Join the `dlt` community on Slack for support, discussions, and recent releases. [Join the community](https://dlthub.com/community)
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 AlloyDB
:
pip install "dlt[postgres]"
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 Pipedrive
to AlloyDB
. You can run the following commands to create a starting point for loading data from Pipedrive
to AlloyDB
:
# create a new directory
mkdir pipedrive_pipeline
cd pipedrive_pipeline
# initialize a new pipeline with your source and destination
dlt init pipedrive postgres
# 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[postgres]>=0.3.5
You now have the following folder structure in your project:
pipedrive_pipeline/
├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── pipedrive/ # folder with source specific files
│ └── ...
├── pipedrive_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.pipedrive]
pipedrive_api_key = "pipedrive_api_key" # please set me up!
[destination.postgres]
dataset_name = "dataset_name" # please set me up!
[destination.postgres.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!
port = 5432
connect_timeout = 15
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 pipedrive_pipeline.py
, as well as a folder pipedrive
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 pipedrive import pipedrive_source
def load_pipedrive() -> None:
"""Constructs a pipeline that will load all pipedrive data"""
# configure the pipeline with your destination details
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='postgres', dataset_name="pipedrive_data"
)
load_info = pipeline.run(pipedrive_source())
print(load_info)
print(pipeline.last_trace.last_normalize_info)
def load_selected_data() -> None:
"""Shows how to load just selected tables using `with_resources`"""
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='postgres', dataset_name="pipedrive_data"
)
# Use with_resources to select which entities to load
# Note: `custom_fields_mapping` must be included to translate custom field hashes to corresponding names
load_info = pipeline.run(
pipedrive_source().with_resources(
"products", "deals", "deals_participants", "custom_fields_mapping"
)
)
print(load_info)
# just to show how to access resources within source
pipedrive_data = pipedrive_source()
# print source info
print(pipedrive_data)
print()
# list resource names
print(pipedrive_data.resources.keys())
print()
# print `persons` resource info
print(pipedrive_data.resources["persons"])
print()
# alternatively
print(pipedrive_data.persons)
def load_from_start_date() -> None:
"""Example to incrementally load activities limited to items updated after a given date"""
pipeline = dlt.pipeline(
pipeline_name="pipedrive", destination='postgres', dataset_name="pipedrive_data"
)
# First source configure to load everything except activities from the beginning
source = pipedrive_source()
source.resources["activities"].selected = False
# Another source configured to activities starting at the given date (custom_fields_mapping is included to translate custom field hashes to names)
activities_source = pipedrive_source(
since_timestamp="2023-03-01 00:00:00Z"
).with_resources("activities", "custom_fields_mapping")
# Run the pipeline with both sources
load_info = pipeline.run([source, activities_source])
print(load_info)
if __name__ == "__main__":
# run our main example
load_pipedrive()
# load selected tables and display resource info
# load_selected_data()
# load activities updated since given date
# load_from_start_date()
Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:
python pipedrive_pipeline.py
4. Inspecting your load result
You can now inspect the state of your pipeline with the dlt
cli:
dlt pipeline pipedrive info
You can also use streamlit to inspect the contents of your AlloyDB
destination for this:
# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline pipedrive 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: Automate your deployment pipeline using GitHub Actions. GitHub Actions allows you to schedule and manage your workflows directly from your GitHub repository.
Deploy with Airflow and Google Composer: Utilize Airflow for orchestrating complex workflows. Google Composer provides a managed Airflow environment to streamline your deployment process.
Deploy with Google Cloud Functions: Leverage Google Cloud Functions for serverless deployment. This allows you to run your pipeline without managing the underlying infrastructure.
Explore more deployment options: Discover additional methods and detailed guides for deploying your pipeline in various environments here.
The running in production section will teach you about:
- Monitor your pipeline: Learn how to effectively monitor your
dlt
pipeline in production to ensure smooth and efficient operation. How to Monitor your pipeline - Set up alerts: Configure alerts to stay informed about the status and performance of your
dlt
pipeline, ensuring you can respond quickly to any issues. Set up alerts - Set up tracing: Implement tracing to gain detailed insights into the execution of your
dlt
pipeline, helping you to debug and optimize performance. And set up tracing
Available Sources and Resources
For this verified source the following sources and resources are available
Source pipedrive
Pipedrive source provides comprehensive data on sales activities, customer interactions, deals, and user information.
Resource Name | Write Disposition | Description |
---|---|---|
activities | merge | Refers to scheduled events or tasks associated with deals, contacts, or organizations |
custom_fields_mapping | replace | Mapping for custom fields in Pipedrive |
deals | merge | Potential sale or transaction that you can track through various stages |
deals_flow | merge | Represents the flow of deals in Pipedrive |
deals_participants | merge | Represents the participants of deals in Pipedrive |
leads | merge | Prospective customers or individuals that have shown interest in a company's products or services |
organizations | merge | Company or entity with which you have potential or existing business dealings |
persons | merge | Individual contact or lead with whom sales deals can be associated |
products | merge | Goods or services that a company sells, which can be associated with deals |
users | merge | Individual with a unique login credential who can access and use the platform |
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