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Create asynchronous data pipelines and deploy to cloud or airflow

Project description

Typhoon Orchestrator

Alt text

Typhoon is a task orchestrator (like Apache Airflow).

Use simple YAML code to easily build asynchronous data pipelines for your ELT workflows.

Save effort by building your data pipelines in Typhoon and then deploy them to run on Airflow. This is risk-free with no-lock-in.

This project is in pre-alpha and subject to fast changes

Why Typhoon for data flows?

  • Elegant YAML and Components - low-code, easy to pick up.
  • Compile to testable Python - robust flows.
  • Functions combine like Lego - effortless to extend for more sources and connections.
  • Compose Components - reduce complex tasks (e.g. CSV→S3→Snowflake) to 1 task.
  • Data flows between tasks - intuitive and easy to build tasks.
  • Flexible deployment options:
    • deploy to Airflow - large reduction in effort, without breaking existing production.
    • deploy to AWS Lambda - completely serverless
name: exchange_rates
schedule_interval: rate(1 day)

    function: functions.exchange_rates_api.get_history
      start_at: !Py $DAG_CONTEXT.interval_start
      end_at: !Py $DAG_CONTEXT.interval_end

    input: exchange_rate
    function: typhoon.filesystem.write_data
      hook: !Hook echo
      data: !MultiStep
        - !Py transformations.xr.flatten_response($BATCH)
        - !Py$1, delimiter='|')
      path: xr_data.csv

Above is an example of two tasks:

  1. Extracting the exchange rates from an API call function for a 1-day range
  2. Writing CSV to a filesystem. This example actually echos it; to put it to S3 change the Hook connection name. Within the edge between task 1 and 2 it transforms the data:
    1. It flattens the data
    2. Then transforms it from a dict to a pipe delimited CSV.

The syntax and functionality is covered fully in the tutorials 1 & 2 below.

Tutorials & Examples included

  • hello_world.yml - list of lists → to CSV (Tutorial Part 1)
  • dwh_flow - Production ready MySQL tables → Snowflake DWH pattern) (Tutorial Part 2)
  • exchange_rates.yml - (above) API → CSV on S3
  • telegram_scraper.yml - telegram group → CSV

Getting started


docker run -it biellls/typhoon bash

# Running isolated commands
docker run --rm biellls/typhoon typhoon status

Local installation

# Install and activate virtualenv
python3 -m venv typhoon_venv

# Clone and install from source
git clone
cd typhoon-orchestrator
python -m pip install ./typhoon-orchestrator[dev]
# Activate bash complete
eval "$(_TYPHOON_COMPLETE=source typhoon)"

# Create a typhoon project
typhoon init typhoon_project
cd typhoon_project

Testing with Airflow

git clone
cd typhoon-orchestrator
docker build -f -t typhoon-af .

TYPHOON_PROJECTS_HOME = "~/typhoon_projects" # Or any other path you prefer
mkdir -p $TYPHOON_PROJECTS_HOME/typhoon_airflow_test
cd $TYPHOON_PROJECTS_HOME/typhoon_airflow_test

typhoon init my_typhoon_project --deploy-target airflow --template airflow_docker
cd my_typhoon_project
docker compose up -d
docker exec -it typhoon-af bash   # Then you're in the typhoon home. Try typhoon status

Example compiling to Airlfow

To compile a DAG to Airflow you need to change the Typhoon.cfg deployment:


Or when you initialise a project:

init typhoon_project --target airflow 

Building your YAML DAG in using typhoon dag build --all you get:

Project details

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