Airflow Unit Tests and Integration Tests
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Updated
Nov 16, 2022 - Python
Airflow Unit Tests and Integration Tests
Gerador de DAGs no Apache Airflow para fazer clipping do Diário Oficial da União.
Queue-Based State Machine - A lightweight workflow execution engine with DAG-based stage orchestration. Unlike simple task queues (like Celery) or advanced orchestrators (like Highway), Stabilize strikes a balance specifically optimized for high-throughput, stateful DAG execution, making it highly suitable for coordinating autonomous AI agents
Data Engineering examples for Airflow, Prefect; dbt for BigQuery, Redshift, ClickHouse, Postgres, DuckDB; PySpark for Batch processing; Kafka for Stream processing
Zero configuration Airflow plugin that let you manage your DAG files.
🎵 LyricWave – AI Music Composer (Proof of Concept) 🎶 A personal project exploring automatic generation of unique MP4 songs. LyricWave blends lyrics with AI-generated melodies and synthetic vocals to experiment with new forms of musical expression. A creative testbed to push your ideas into sound. 🚀🎧
Orchestration of data science and earth observation models in Apache Airflow, scale-up with Celery Executor, experiment with jupyter notebook using a docker containers composition
This is my Apache Airflow Local development setup on Windows 10 WSL2/Mac using docker-compose. It will also include some sample DAGs and workflows.
Apache Airflow Guide
My self-learning about Apache Airflow
Orchestrate your Databricks notebooks in Airflow and execute them as Databricks Workflows
An end-to-end Twitter Data Pipeline that extracts data from Twitter and loads it into AWS S3.
Here I added 9 projects which have been made by me during my apprenticeship in Yandex.Practicum as data engineer.
This project creates a basic web service for solving image-based CAPTCHAs. Using the Flask framework, it allows users to upload CAPTCHA images and employs an Optical Character Recognition (OCR) pipeline to extract the embedded text.
A starting point for a data stack using Python, Apache Airflow and Metabase.
An ETL Data Pipelines Project that uses AirFlow DAGs to extract employees' data from PostgreSQL Schemas, load it in AWS Data Lake, Transform it with Python script, and Finally load it into SnowFlake Data warehouse using SCD type 2.
End-to-end data engineering processes for the NIGERIA Health Facility Registry (HFR). The project leveraged Selenium, Pandas, PySpark, PostgreSQL and Airflow
EPL Fotmob Data Visualizations
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