ploomber | fastest ⚡️ way to build data pipelines | BPM library

by ploomber Python Version: 0.23.2 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ploomber Summary

ploomber is a Python library typically used in Automation, BPM applications. ploomber has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install ploomber' or download it from GitHub, PyPI.

Join our community | Newsletter | Contact us | Docs | Blog | Website | YouTube. Ploomber is the fastest way to build data pipelines ️. Use your favorite editor (Jupyter, VSCode, PyCharm) to develop interactively and deploy ️ without code changes (Kubernetes, Airflow, AWS Batch, and SLURM). Do you have legacy notebooks? Refactor them into modular pipelines with a single command.

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Quality

Security

License

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Support

ploomber has a medium active ecosystem.

It has 3074 star(s) with 210 fork(s). There are 29 watchers for this library.

There were 2 major release(s) in the last 6 months.

There are 131 open issues and 703 have been closed. On average issues are closed in 67 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ploomber is 0.23.2

Quality

ploomber has 0 bugs and 0 code smells.

Security

ploomber has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ploomber code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ploomber is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ploomber releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

ploomber saves you 9830 person hours of effort in developing the same functionality from scratch.

It has 20034 lines of code, 2144 functions and 263 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ploomber and discovered the below as its top functions. This is intended to give you an instant insight into ploomber implemented functionality, and help decide if they suit your requirements.

Derives a class from the base class
Show info
Load the DAG
Print a separable separator
Run the task
Write text to path
Convert a notebook into a text file
Convert to notebook
Decorator to handle CLI endpoint
Return a default meta dict
Load the pipeline
Returns the environment variable for a given path
Build the DAG
Add a file to the pipeline
Extract created relations from a SQLite database
Return debug information about this task
Run a primitive function
Build a subprocess in subprocess
Preprocess raw data
Set the execution status of the DAG
Render the plot as HTML
Create a DAG from a list of functions
Create the task CLI
Create a scaffold
Builds a fully - qualified dag
Run a subprocess

Get all kandi verified functions for this library.

ploomber Key Features

No Key Features are available at this moment for ploomber.

ploomber Examples and Code Snippets

MLops review

Jupyter Notebook

Lines of Code : 15

License : No License

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   #!/bin/bash
   #SBATCH --job-name={{name}}
   #SBATCH --output=result.out
   #
   
   # load anaconda module
   module purge
   ml Anaconda3/2021.05
   # activate prepared environment
   
   eval "$(conda shell.bash hook)"
   conda activate $HOME/

Ploomber sample projects,Running examples

Jupyter Notebook

Lines of Code : 10

License : Permissive (Apache-2.0)

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pip install ploomber

# list examples
ploomber examples

# download example with name
ploomber examples --name {name}

# example
ploomber examples --name templates/mlflow

How to mass produce reports with jupyter notebooks?

Python

Lines of Code : 21

License : Strong Copyleft (CC BY-SA 4.0)

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dag = DAG()

get_data = PythonCallable(_get_data,
                          product=File('raw.parquet'),
                          dag=dag)

clean_data = PythonCallable(_clean_data,
                            product=File('clean.parquet')