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Python Guide: Loading Slack Data to BigQuery with dlt Library

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This page provides technical documentation on using the open-source Python library, dlt, to load data from Slack to BigQuery. Slack is a business messaging app that centralizes information, making it readily accessible to users. BigQuery, on the other hand, is a serverless enterprise data warehouse that is cost-effective and scalable across clouds. Leveraging dlt, we can efficiently extract data from Slack and load it into BigQuery for further analysis and usage. More information about Slack can be found at

dlt Key Features

  • Google BigQuery Destination: Learn how to set up and use Google BigQuery as a destination for your data pipeline with dlt. Learn more
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more
  • Why use dlt?: Discover the main reasons to use dlt, such as automated maintenance, compatibility with Python, and a user-friendly interface. Learn more
  • Slack Verified Source: Use Slack as a verified source for your dlt data pipeline. Learn more
  • Tutorial: Follow a step-by-step tutorial to learn how to efficiently use dlt to build a data pipeline. Learn more

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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack bigquery
# 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── slack/ # folder with source specific files
│ └── ...
├── # 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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
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

access_token = "access_token" # please set me up!

location = "US"

project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 BigQuery 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, 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='bigquery', 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 =

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='bigquery', dataset_name="slack_data"

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

load_info =

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

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

source = slack_source(

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"])


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


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 BigQuery 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 provides an easy way to deploy your pipelines using Github Actions. This method is free and allows you to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your pipelines using Airflow. This method creates an Airflow DAG for your pipeline script and provides you with the necessary environment variables and secrets.
  • Deploy with Google Cloud Functions: dlt allows you to deploy your pipelines using Google Cloud Functions. This method is ideal for deploying lightweight, single-purpose functions that respond to cloud events.
  • Other Deployment Methods: There are other deployment methods available for dlt pipelines. You can find more information about these methods here.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides robust monitoring capabilities to ensure your pipeline is running smoothly. You can access detailed information about your pipeline's performance and status. Learn more about how to monitor your pipeline.
  • Set Up Alerts: Stay updated with real-time alerts. dlt allows you to set up alerts that notify you about any critical events or changes in your pipeline. Check out the guide on how to set up alerts.
  • Enable Tracing: Gain insights into your pipeline's execution with dlt's tracing capabilities. Tracing allows you to track the execution of your pipeline, helping you identify any issues and optimize performance. Learn about setting 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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