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Loading Slack Data to Redshift using Python's dlt Library

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This page provides technical documentation on how to load data from Slack to Redshift using the open-source Python library, dlt. Slack is a business-oriented messaging app that keeps teams connected to the information they need. On the other hand, Redshift is Amazon's fully managed data warehouse service in the cloud, capable of handling data from a few hundred gigabytes to over a petabyte. By leveraging dlt, you can effectively manage the data transfer from Slack to Redshift. For additional details about Slack, please visit https://slack.com.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance with schema inference and evolution alerts. This makes maintenance simple and efficient. Learn more
  • Versatility: dlt can run wherever Python runs. This includes Airflow, serverless functions, notebooks, and more. This makes it extremely versatile and adaptable to various infrastructures. Learn more
  • User-Friendly Interface: dlt provides a user-friendly, declarative interface that is accessible for beginners while still empowering senior professionals. Learn more
  • Alerting: dlt allows you to configure alerts for your data pipelines, providing you with rich information on executed pipelines, including encountered errors and exceptions. Learn more
  • Community Support: dlt has a vibrant community where you can ask questions, share your experiences, and learn from others. Join the 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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack redshift
# 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[redshift]>=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.redshift.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 = 5439
connect_timeout = 15

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 Redshift 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='redshift', 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='redshift', 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='redshift', 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 Redshift 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 supports deployment using Github Actions. This method involves setting up a CI/CD runner that can be scheduled to run at specific times.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow. This option is particularly useful if you are using Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: If you're working within the Google Cloud Platform, dlt pipelines can be deployed using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
  • Other Deployment Methods: dlt also supports other deployment methods. You can find more details on the deployment guide.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Keep a close eye on your pipeline's performance and health with dlt's monitoring capabilities. This guide will show you how to track your pipeline's progress, status, and other key metrics. Find out more here.
  • Set up alerts: Stay informed about any issues or changes in your pipeline. dlt allows you to set up alerts that notify you when specific events occur in your pipeline. Learn how to set up alerts here.
  • Set up tracing: Understand and optimize your pipeline's performance with dlt's tracing feature. Tracing allows you to track the execution of your pipeline and identify any potential bottlenecks or issues. Learn more about setting up tracing here.

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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