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Python Data Loading from slack to azure synapse with dlt

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This page provides technical documentation for using the open-source Python library dlt to load data from Slack to Azure Synapse. Slack is a business messaging app that consolidates vital information for easy access. On the other hand, Azure Synapse is an expansive analytics service, combining enterprise data warehousing and Big Data analytics. By using dlt, you can seamlessly transfer data between these platforms. More details about Slack can be found at https://slack.com.

dlt Key Features​

  • Azure Synapse Integration: dlt provides seamless integration with Azure Synapse, a scalable analytics service. Simply install the dlt library with Synapse dependencies using the command pip install dlt[synapse]. Learn more
  • Slack Verified Source: dlt includes a verified source for Slack API, allowing you to load data from Slack to your destination of choice. Learn more
  • Robust Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts, contributing to better data management practices and overall data governance. Learn more
  • Alerting Capabilities: dlt allows for the setup of alerting for pipelines, with alerts triggered by specific actions. It supports integrations with Sentry for error tracking and Slack for sending messages. Learn more
  • Community Support: dlt has a growing community where you can find recent releases or discuss what you can build with dlt. 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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir slack_pipeline
cd slack_pipeline
# initialize a new pipeline with your source and destination
dlt init slack synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 Azure Synapse 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='synapse', 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='synapse', 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='synapse', 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 Azure Synapse 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​

  • Github Actions: dlt can be deployed using Github Actions. This CI/CD runner allows you to schedule when your pipeline should run using a cron schedule expression.
  • Airflow: You can deploy dlt using Airflow, a platform used to programmatically author, schedule and monitor workflows. Google Composer, a managed Airflow environment, can also be used.
  • Google Cloud Functions: dlt supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: There are several other ways to deploy dlt. You can find more information on these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: Once your dlt pipeline is set up and running, it's crucial to keep an eye on its performance. The Monitoring Guide provides detailed instructions on how to monitor your pipeline effectively.
  • Set Up Alerts: To stay informed about any issues or changes in your pipeline, setting up alerts is a must. The Alerting Guide walks you through the process of setting up alerts for your dlt pipeline.
  • Implement Tracing: Tracing is a valuable tool for identifying and diagnosing potential problems in your pipeline. The Tracing Guide shows you how to set up tracing for your dlt pipeline.

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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