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Python Guide: Loading Slack Data to SQL Server with dlt

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This page provides technical documentation on using the open-source Python library, dlt, to load data from Slack to Microsoft SQL Server. Slack is a business-oriented messaging application that facilitates access to crucial information. Microsoft SQL Server is a relational database management system (RDBMS) that allows applications and tools to connect and interact using Transact-SQL. The dlt library simplifies the process of transferring data from Slack to Microsoft SQL Server, making it an essential tool for data management. Further details about Slack can be found at https://slack.com.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance with schema inference and evolution alerts. This feature simplifies the maintenance process and ensures the integrity of your data. Learn more about it here.

  • Run it Anywhere: dlt can be run wherever Python is supported. This includes Airflow, serverless functions, and notebooks. No need for external APIs, backends, or containers. It scales on both micro and large infrastructures. Check out the Getting started guide for more details.

  • User-friendly Interface: dlt provides a user-friendly, declarative interface that is easy for beginners to understand while still offering powerful features for senior professionals. See the Tutorial to learn how to build a pipeline that loads data from an API.

  • Alerting: dlt supports alerting for monitoring the health of your data product. This feature allows you to receive notifications about the running status of your pipeline. Find out more about this feature here.

  • Support for Various Destinations: dlt supports a wide range of destinations like Microsoft SQL Server, DuckDB, and more. This flexibility allows you to choose the destination that best suits your needs. For example, you can learn how to set up a pipeline with DuckDB 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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack mssql
# 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[mssql]>=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.mssql.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Microsoft SQL Server 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='mssql', 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='mssql', 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='mssql', 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 Microsoft SQL Server 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 through Github Actions. This method leverages the power of Github's CI/CD runner to automate your pipeline deployment.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow. This method is especially useful if you are using Google Composer, a managed Airflow environment provided by Google.
  • Deploy with Google Cloud Functions: dlt allows you to deploy your pipelines using Google Cloud Functions. This method is ideal for serverless deployment of your pipelines.
  • Other Deployment Methods: dlt also supports various other deployment methods. You can find more information about these methods here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: dlt provides comprehensive tools to monitor your data pipeline. It allows you to inspect the load information and trace, save and alert on schema changes. Check out the detailed guide here.
  • Set up alerts: With dlt, you can set up alerts to keep track of your pipeline's status and performance. It enables you to detect any issues early and take corrective action promptly. Learn more about it here.
  • Set up tracing: Tracing is a powerful feature in dlt that helps you understand the execution flow of your pipeline. It provides timing information on extract, normalize, and load steps. Get started with 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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