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Python Guide: Loading Slack Data to AWS Athena using dlt Library

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This page provides technical documentation about loading data from slack, a business messaging app that facilitates information sharing, to aws athena, an interactive query service from Amazon that simplifies data analysis in Amazon S3 using standard SQL. Our implementation also supports iceberg tables. The process is facilitated by an open source Python library named dlt. More information about the source can be found at https://slack.com.

dlt Key Features​

  • Asana API: dlt offers a verified source for the Asana API, allowing users to easily create, assign, and track tasks, set deadlines, and communicate with each other in real-time. Learn more
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more
  • Alerting: dlt provides a comprehensive alerting system for your pipelines, including the ability to configure alerts via Sentry and Slack. Learn more
  • AWS Athena / Glue Catalog: dlt supports AWS Athena as a destination, allowing users to store data as parquet files in S3 buckets and create external tables in AWS Athena. Learn more
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes, allowing them to take necessary actions such as reviewing and validating the changes, updating downstream processes, or performing impact analysis. Learn 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 AWS Athena:

pip install "dlt[athena]"

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

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

[destination.athena.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!

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 AWS Athena 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='athena', 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='athena', 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='athena', 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 Athena 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 easily deployed using Github Actions. This CI/CD runner is available for free and can be scheduled using a cron schedule expression. For more information, check out the guide on how to deploy a pipeline with Github Actions.
  • Deploy with Airflow: dlt also supports deployment with Airflow. This method creates an Airflow DAG for your pipeline script, making the process trivial. Learn more about how to deploy a pipeline with Airflow.
  • Deploy with Google Cloud Functions: For deploying with Google Cloud Functions, dlt provides a step-by-step guide. This method allows you to run your pipeline in response to events without needing to manage a server. Find out more about how to deploy a pipeline with Google Cloud Functions.
  • Other Deployment Methods: dlt offers various other methods for deploying your pipeline. Check out other ways to deploy a pipeline for more information.

The running in production section will teach you about:​

  • Monitor your pipeline: After setting up your dlt pipeline, you can efficiently monitor its performance and progress. This is crucial for identifying and resolving any issues that may arise during the pipeline's operation. Find out more about monitoring your pipeline here.
  • Set up alerts: dlt allows you to set up alerts that notify you of any significant events or changes in your pipeline. This feature helps you stay on top of your pipeline's performance and promptly address any issues. Learn how to set up alerts here.
  • Set up tracing: Tracing is an essential feature for understanding the behavior of your pipeline and identifying any potential bottlenecks or issues. With dlt, you can easily set up tracing for your pipeline. Find out more about setting 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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