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Load Slack Data to EDB BigAnimal Using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to EDB BigAnimal. You can get the connection string for your EDB BigAnimal database as described in the EDB BigAnimal Docs.

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Slack is a messaging app for business that connects people to the information they need. EDB BigAnimal is a fully managed database-as-a-service that runs in your cloud account or BigAnimal's cloud account, operated by one of the builders of Postgres. BigAnimal simplifies the process of setting up, managing, and scaling databases. It supports PostgreSQL or EDB Postgres Advanced Server with Oracle compatibility and offers distributed high-availability cluster types for geographically distributed databases. This documentation will guide you on how to load data from Slack to EDB BigAnimal using the open-source python library called dlt. For more details on Slack, visit Slack.

dlt Key Features​

  • Pipeline Metadata Utilization: dlt pipelines leverage metadata to provide governance capabilities. This metadata includes load IDs, enabling incremental transformations and data vaulting by tracking data loads and facilitating data lineage and traceability. Read more
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Schemas define the structure of normalized data and guide the processing and loading of data. Read more
  • Schema Change Alerts: dlt enables proactive governance by alerting users to schema changes. When modifications occur in the source data’s schema, dlt notifies stakeholders, allowing them to take necessary actions. Read more
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, including running extraction, normalization, and load in parallel. Read more
  • Alerting: dlt provides robust alerting mechanisms to monitor the health of data products, including integration with Sentry and Slack for notifications and tracing. 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 EDB BigAnimal:

pip install "dlt[postgres]"

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

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

[destination.postgres.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 = 5432
connect_timeout = 15

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 EDB BigAnimal 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(replies: bool = False) -> None:
    """Load all resources from slack without any selection of channels."""

    pipeline = dlt.pipeline(
        pipeline_name="slack", destination='postgres', dataset_name="slack_data"
    )

    source = slack_source(
        page_size=1000,
        start_date=datetime(2023, 9, 1),
        end_date=datetime(2023, 9, 8),
        replies=replies,
    )

    # Uncomment the following line to load only the access_logs resource. It is not selected
    # 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='postgres', 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='postgres', 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()

    # load all resources with replies
    # load_all_resources(replies=True)

    # 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 EDB BigAnimal 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: Learn how to deploy your dlt pipelines using GitHub Actions for a CI/CD approach. Read more
  • Deploy with Airflow and Google Composer: Discover how to use Airflow and Google Composer to manage your dlt pipelines. Read more
  • Deploy with Google Cloud Functions: Find out how to deploy your dlt pipelines using Google Cloud Functions for serverless execution. Read more
  • Explore other deployment options: Check out various other methods to deploy your dlt pipelines. Read more

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operation and quick issue resolution. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified about important events and potential issues in your dlt pipeline. Set up alerts
  • Set up tracing: Implement tracing to gain insights into the performance and behavior of your dlt pipeline. And set up tracing

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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