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Python Data Loading from slack to databricks using dlt Library

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This page provides technical documentation on how to load data from Slack, a business messaging app that centralizes information access, to Databricks, a unified data analytics platform conceived by the original creators of Apache Spark™. This process is facilitated using dlt, an open-source Python library. The documentation will guide you through the necessary steps, offering a comprehensive understanding of how dlt interacts with Slack and Databricks to streamline data transfer and analytics. For more information about Slack, please visit https://slack.com.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines utilize metadata for governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more.
  • Schema Enforcement and Curation: The library allows users to enforce and curate schemas, ensuring data consistency and quality. Read more.
  • Schema Evolution: dlt provides proactive governance by alerting users to schema changes, enabling them to take necessary actions. Read more.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, including parallelization and memory optimization. Read more.
  • Automated Maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Read 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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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='databricks', 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='databricks', 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='databricks', 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 Databricks 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 deployed with Github Actions. This is a CI/CD runner that is basically free to use. You need to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy dlt with Airflow. This method creates an Airflow DAG for your pipeline script that you should customize. It works with any Airflow instance.
  • Deploy with Google Cloud Functions: dlt can be deployed with Google Cloud Functions. This allows you to execute your data pipeline in response to specific cloud events.
  • Other Deployment Methods: There are other ways to deploy dlt as well. You can find more information on the deploy a pipeline page.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides comprehensive monitoring capabilities to ensure your pipeline is running smoothly and efficiently. You can track the progress of your pipeline, inspect the load packages, and even save the load information for future reference. Learn more about how to monitor your pipeline here.
  • Set Up Alerts: Stay ahead of any potential issues with dlt's alerting feature. You can set up alerts to notify you about any changes in your pipeline, such as schema changes. This allows you to address any issues promptly and keep your pipeline running smoothly. Find out how to set up alerts here.
  • Set Up Tracing: dlt allows you to trace the runtime of your pipeline. This feature provides timing information on extract, normalize, and load steps, and also displays all the config and secret values. It's a valuable tool for debugging and optimizing your pipeline. Learn how to set 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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