Displaying 1 to 8 from 8 results

pipeline - PipelineAI: Real-Time Enterprise AI Platform

  •    HTML

Each model is built into a separate Docker image with the appropriate Python, C++, and Java/Scala Runtime Libraries for training or prediction. Use the same Docker Image from Local Laptop to Production to avoid dependency surprises.

Agile_Data_Code_2 - Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

  •    Jupyter

Like my work? I am Principal Consultant at Data Syndrome, a consultancy offering assistance and training with building full-stack analytics products, applications and systems. Find us on the web at datasyndrome.com. There is now a video course using code from chapter 8, Realtime Predictive Analytics with Kafka, PySpark, Spark MLlib and Spark Streaming. Check it out now at datasyndrome.com/video.

awesome-apache-airflow - Curated list of resources about Apache Airflow


This is a curated list of resources about Apache Airflow (incubating). Please feel free to contribute any items that should be included. Items are generally added at the top of each section so that more fresh items are featured more prominently.

airflow-docker - Apache Airflow Docker Image.

  •    Python

This repository contains Dockerfile of apache-airflow for Docker's automated build published to the public Docker Hub Registry. Pull the image from the Docker repository.

airflow-operator - Kubernetes custom controller and CRDs to managing Airflow

  •    Go

This is not an officially supported Google product. The Airflow Operator is still under active development and has not been extensively tested in production environment. Backward compatibility of the APIs is not guaranteed for alpha releases.

dag-factory - Dynamically generate Apache Airflow DAGs from YAML configuration files

  •    Python

dag-factory is a library for dynamically generating Apache Airflow DAGs from YAML configuration files. To install dag-factory run pip install dag-factory. It requires Python 3.6.0+ and Apache Airflow 1.9+.

discreETLy - ETLy is an add-on dashboard service on top of Apache Airflow.

  •    Python

DiscreETLy is an add-on dashboard service on top of Apache Airflow. It is a user friendly UI showing status of particular DAGs. Moreover, it allows the users to map Tasks within a particular DAG to tables available in any system (relational and non-relational) via friendly yaml definition. DiscreETLy provides fuctionality for monitoring DAGs status as well as optional communication with services such as Prometheus or InfluxDB. Minimal setup required to run the dashboard requires docker. You can find docker installation instructions on official docker website.