pyDag
data-engineer-challenge
Our great sponsors
| pyDag | data-engineer-challenge | |
|---|---|---|
| 2 | 3 | |
| 24 | 25 | |
| - | - | |
| 0.0 | 1.8 | |
| over 1 year ago | almost 2 years ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyDag
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Data Engineering Projects for Beginners
Scheduling Big Data Workloads and Data Pipelines in the Cloud with pyDag
- How to build a DAG based Task Scheduling tool for Multiprocessor systems using python
data-engineer-challenge
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Data Engineering Projects for Beginners
Design, Development and Deployment of a simple Data Pipeline
- Design, Development and Deployment of a simple Data Pipeline
What are some alternatives?
distance-metrics - Distance metrics are one of the most important parts of some machine learning algorithms, supervised and unsupervised learning, it will help us to calculate and measure similarities between numerical values expressed as data points
docker-livy - Dockerizing and Consuming an Apache Livy environment
livyc - Apache Spark as a Service with Apache Livy Client
Dropout-Students-Prediction - The goal of this project is to identify students at risk of dropping out the school
pubsub2inbox - Pubsub2Inbox is a versatile, multi-purpose tool to handle Pub/Sub messages and turn them into email, API calls, GCS objects, files or almost anything.
p_tqdm - Parallel processing with progress bars
text-analysis-speeches-amlo - Text analysis of the speeches, conferences and interviews of the current president of Mexico
breaking_cycles_in_noisy_hierarchies - breaking cycles in noisy hierarchies
data-engineering-challenge-th - Dockerizing a Python Script for Web Scraping and consume the scraped data using FastApi (www.metroscubicos.com)