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Python dlt Library: Loading Data from slack to snowflake

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This is a technical guide for utilizing dlt, an open-source Python library, to load data from Slack to Snowflake. Slack is a business messaging app that centralizes information flow, while Snowflake is a cloud-based data warehousing platform that excels in storing, processing, and analyzing large data volumes. By leveraging dlt, we can efficiently transport data from Slack to Snowflake for further analysis. For additional information about Slack, please visit https://slack.com.

dlt Key Features​

  • Scalability and Performance: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines, such as running extraction, normalization, and load in parallel, and writing sources and resources that run in parallel via thread pools and async execution. Read more about performance.

  • Data Governance: dlt pipelines provide robust governance support through utilization of pipeline metadata, schema enforcement and curation, and schema change alerts, contributing to better data management practices, compliance adherence, and overall data governance. Read more about governance.

  • Data Extraction: Extracting data with dlt is simple and scalable, leveraging iterators, chunking, and parallelization techniques, and utilizing implicit extraction DAGs for efficient API calls for data enrichments or transformations. Read more about data extraction.

  • Snowflake Destination: dlt supports Snowflake as a destination, accepting three authentication types and providing detailed instructions for each. Read more about Snowflake destination.

  • Community Support: dlt has a growing community that supports many features and use cases needed by the community. You can join their Slack community to find recent releases or discuss what you can build with dlt. Join the dlt 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 Snowflake:

pip install "dlt[snowflake]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack snowflake
# 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[snowflake]>=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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # 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 Snowflake 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='snowflake', 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='snowflake', 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='snowflake', 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 Snowflake 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 allows you to prepare your pipeline for deployment using GitHub Actions, a CI/CD runner that is basically free. You can specify when the GitHub Action should run using a cron schedule expression. Learn more about it here.
  • Deploy with Airflow: dlt can also be deployed with Airflow, a platform to programmatically author, schedule and monitor workflows. You can create an Airflow DAG for your pipeline script that you should customize. Detailed instructions can be found here.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services. You can find more about it here.
  • Other Deployment Options: Apart from the above mentioned options, dlt supports various other deployment options. You can find more about them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep a close eye on your pipeline's performance and status. Get detailed insights about your pipeline's operation with the help of this guide.
  • Set Up Alerts: Stay informed about your pipeline's health by setting up alerts. dlt makes it easy to set up alerts to notify you of any issues in your pipeline. Learn how to set up alerts with this guide.
  • Set Up Tracing: Trace the execution of your pipeline to understand its behavior better. dlt provides powerful tracing capabilities to help you debug and optimize your pipeline. Find out how to set up tracing with this guide.

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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