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Python Data Loading from slack to motherduck using dlt

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Welcome to our technical documentation, which guides you through the process of loading data from slack, a business messaging application, to motherduck, an in-process analytical database with a rich SQL dialect. This process is facilitated by dlt, an open-source Python library. In this guide, we'll provide step-by-step instructions on how to use dlt to efficiently transfer data from slack to motherduck. For more information about slack, please visit Slack's official website.

dlt Key Features​

  • MotherDuck Destination: dlt supports MotherDuck as a destination for your data pipelines. It provides detailed setup instructions and supports various write dispositions. Learn more
  • Tutorial Guide: dlt provides a comprehensive tutorial that guides you through the process of building a data pipeline, using the GitHub API and DuckDB as examples. Follow the tutorial
  • Slack Community Support: dlt has a vibrant Slack community where you can get help deploying sources or figuring out how to run them in your data stack. Join the community
  • Alerting Mechanism: dlt provides robust alerting mechanisms that can be configured to notify you about the status of your pipelines. It supports various platforms like Sentry and Slack for alert notifications. Set up alerts
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn about governance support

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 MotherDuck:

pip install "dlt[motherduck]"

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 Slack to MotherDuck. You can run the following commands to create a starting point for loading data from Slack to MotherDuck:

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack motherduck
# 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[motherduck]>=0.3.12

You now have the following folder structure in your project:

slack_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── slack/                   # folder with source specific files
│   └── ...
├── slack_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.slack]
access_token = "access_token" # please set me up!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the Slack source in our docs.
  • Read more about setting up the MotherDuck 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 slack_pipeline.py, as well as a folder slack 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:


"""Pipeline to load slack into duckdb."""

from typing import List

import dlt
from pendulum import datetime
from slack import slack_source


def load_all_resources() -> None:
    """Load all resources from slack without any selection of channels."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='motherduck', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=1000, start_date=datetime(2023, 9, 1), end_date=datetime(2023, 9, 8)
    )

    # Uncomment the following line to load only the access_logs resource. It is not selectes
    # by default because it is a resource just available on paid accounts.
    # source.access_logs.selected = True

    load_info = pipeline.run(
        source,
    )
    print(load_info)


def select_resource(selected_channels: List[str]) -> None:
    """Execute a pipeline that will load the given Slack list of channels with the selected
    channels incrementally beginning at the given start date."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='motherduck', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=20,
        selected_channels=selected_channels,
        start_date=datetime(2023, 9, 1),
        end_date=datetime(2023, 9, 8),
    ).with_resources("channels", "1-announcements", "dlt-github-ci")

    load_info = pipeline.run(
        source,
    )
    print(load_info)


def get_users() -> None:
    """Execute a pipeline that will load Slack users list."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='motherduck', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=20,
    ).with_resources("users")

    load_info = pipeline.run(
        source,
    )
    print(load_info)


if __name__ == "__main__":
    # Add your desired resources to the list...
    # resources = ["access_logs", "conversations", "conversations_history"]

    # load_all_resources()
    # select_resource(selected_channels=["dlt-github-ci"])

    # select_resource(selected_channels=["1-announcements", "dlt-github-ci"])

    get_users()

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python slack_pipeline.py

4. Inspecting your load result​

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

dlt pipeline slack info

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

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline slack 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: dlt can be easily deployed with Github Actions. This allows you to automate your workflows and run your pipeline on a schedule. It's as simple as running the dlt deploy <script>.py github-action --schedule "*/30 * * * *" command. Learn more
  • Deploy with Airflow: dlt also supports deployment with Airflow. This is especially useful if you're using Google Composer, a managed Airflow environment provided by Google. To deploy with Airflow, you need to run the dlt deploy <script>.py airflow-composer command. Learn more
  • Deploy with Google Cloud Functions: If you're using Google Cloud, you can deploy your dlt pipeline with Google Cloud Functions. This allows you to run your pipeline in response to events without having to manage a server. Learn more
  • Other Deployment Options: dlt supports a variety of other deployment options. You can find additional information and guides on how to deploy a pipeline with dlt in the documentation. Learn more

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides robust tools for monitoring your pipeline's performance and status. You can track your pipeline's progress, inspect load packages, and even save runtime traces for further analysis. Find out more about how to monitor your pipeline.
  • Set up alerts: Stay informed on the status of your pipeline with dlt's alerting features. You can set up alerts for various events such as job failures, schema changes, and more. Learn how to set up alerts.
  • Set up tracing: Tracing is a powerful feature in dlt that provides detailed insights into your pipeline's execution. It helps you understand the timing of extract, normalize, and load steps, and also reveals the source of config and secret values. Discover how to set up tracing.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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