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Python Guide: Loading Data from slack to aws s3 using dlt

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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Welcome to our technical documentation on how to load data from slack, a business messaging app, to aws s3, a remote file system and bucket storage service. We will be utilizing dlt, an open-source Python library, to facilitate this process. dlt acts as an intermediary, abstracting file operations and serving as a staging area before the data reaches its final destination, in this case, aws s3. This guide will assist you in quickly building a data lake with dlt. For more information about slack, please visit https://slack.com.

dlt Key Features​

  • Filesystem & Buckets: DLT uses remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage to store data. It primarily serves as a staging area for other destinations, but can also be used to quickly build a data lake. More details can be found here.

  • Advanced Usage with dlt init: DLT provides advanced options for initialization, such as deploying from a specific branch or another repository. This feature allows for greater flexibility and control over the source of data. More information can be found here.

  • Data Management: By default, DLT leaves the loaded packages intact for full querying and inspection after load. This behavior can be configured so the successfully completed jobs are deleted from the loaded package, leaving behind a minimum amount of data. More details can be found here.

  • Slack Integration: DLT provides basic support for sending Slack messages, allowing for easy communication and updates within a team. This feature can be configured via secrets.toml or environment variables. More information can be found here.

  • Write Disposition & Data Loading: DLT handles write dispositions and stores all the files in a single folder with the name of the dataset. The filename layout can be customized according to the user's preference. More details can be found 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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from Slack to AWS S3:

# create a new directory
mkdir my-slack-pipeline
cd my-slack-pipeline
# initialize a new pipeline with your source and destination
dlt init slack filesystem
# 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[filesystem]>=0.3.12

You now have the following folder structure in your project:

my-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:

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

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Slack source in the dlt verifed sources documentation.

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='filesystem', 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='filesystem', 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='filesystem', 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 AWS S3 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 using Github Actions. This is a CI/CD runner that you can use for free. You need to specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can deploy your dlt pipeline using Airflow. This is a managed environment provided by Google. It will create an Airflow DAG for your pipeline script that you should customize.
  • Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: There are several other ways to deploy your dlt pipeline. You can find more information on the dlt documentation page.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides comprehensive monitoring capabilities to keep track of your pipeline's performance and status. Learn how to monitor your pipeline here.
  • Set Up Alerts: Stay informed about your pipeline's health with dlt's alerting feature. It allows you to set up alerts based on specific conditions to ensure you're promptly notified of any issues. Learn how to set up alerts here.
  • Set Up Tracing: Tracing in dlt provides detailed information about the execution of your pipeline, helping you understand its behavior and diagnose issues. 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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