Skip to main content

Loading Slack Data to Dremio Using Python's dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Violetta.

Welcome to our technical documentation page on how to load data from Slack to Dremio using the open-source Python library, dlt. Slack is a business messaging app that brings people closer to the information they need. On the other hand, Dremio is a comprehensive data lakehouse solution that caters to leaders at all stages of their data journey, offering flexibility, scalability, and performance. This guide will walk you through the process of utilizing dlt to facilitate the data transfer from Slack to Dremio. For more details about Slack, please visit https://slack.com.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance through schema inference, evolution, and alerts. It allows for simple maintenance with short declarative code. Read more about it here.

  • Scalability and Fine-tuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines, such as running extraction, normalization and load in parallel, and fine-tuning memory buffers, intermediary file sizes, and compression options. Learn more about it here.

  • Effective Data Extraction: dlt makes data extraction simple by allowing users to decorate their data-producing functions with loading or incremental extraction metadata. It also offers scalability through iterators, chunking, and parallelization, and utilizes implicit extraction Directed Acyclic Graphs (DAGs) for efficient API calls for data enrichments or transformations. Read more about it here.

  • Alerting: dlt allows users to set up alerts for their pipelines. Users can configure Sentry DSN to start receiving rich information on executed pipelines, including encountered errors and exceptions. Users can also send messages to Slack for alerting. Learn more about it here.

  • Community Support: dlt has a growing community on Slack where users can discuss recent releases or what they can build with dlt. Users can also give the library a star on GitHub, ask questions, share how they use the library, report problems, and make feature requests. Join the community here.

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 Dremio:

pip install "dlt[dremio]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack dremio
# 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[dremio]>=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.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.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 = 32010

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 Dremio 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(replies: bool = False) -> None:
    """Load all resources from slack without any selection of channels."""

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

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

    # Uncomment the following line to load only the access_logs resource. It is not selected
    # 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='dremio', 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='dremio', 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()

    # load all resources with replies
    # load_all_resources(replies=True)

    # 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 Dremio 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 easy deployment of your pipelines using Github Actions. You simply need to specify when the Github Action should run using a cron schedule expression.
  • Deploy with Airflow and Google Composer: dlt provides support for deploying your pipelines with Airflow and Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: If you prefer serverless deployment, dlt has got you covered with Google Cloud Functions. You can deploy your pipelines as Google Cloud Functions using the dlt deploy command.
  • Other Deployment Options: dlt also supports other deployment options. Check out the deployment guide for more information.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides you with the ability to monitor your pipeline in production. You can inspect the load information and trace, save and alert on schema changes, and much more. Check out the guide on how to monitor your pipeline for more details.
  • Set Up Alerts: With dlt, you can set up alerts to notify you about any issues or changes in your pipeline. This feature allows you to respond quickly to any potential problems. Learn more about how to set up alerts.
  • Set Up Tracing: dlt also offers the ability to set up tracing for your pipeline. This feature provides you with detailed information about the execution of your pipeline, helping you to identify and resolve issues more effectively. Check out the guide on 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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.