SmartPipeline VS patterns-devkit

Compare SmartPipeline vs patterns-devkit and see what are their differences.

SmartPipeline

A framework for rapid development of robust data pipelines following a simple design pattern (by giacbrd)
Pipelines pipeline-framework Data Analysis Data Mining Machine Learning task-queue data-pipelines Pipeline Data Science design-patterns Data processing Reproducibility data-analytics Workflow ETL Mlops dataops
Source Code
smartpipeline.readthedocs.io
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patterns-devkit

Data pipelines from re-usable components (by patterns-app)
Data Science Data Analysis Pipelines ETL etl-pipeline etl-framework Functional Reactive Programming data-engineering SQL Immutability data-pipeline data-pipelines etl-pipelines
Source Code
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SmartPipeline patterns-devkit
1 5
22 106
- 0.0%
7.5 2.9
2 months ago about 1 year ago
Python Python
GNU Lesser General Public License v3.0 only BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

SmartPipeline

Posts with mentions or reviews of SmartPipeline. We have used some of these posts to build our list of alternatives and similar projects.

patterns-devkit

Posts with mentions or reviews of patterns-devkit. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing SmartPipeline and patterns-devkit you can also consider the following projects:

Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai

pyspark-example-project - Implementing best practices for PySpark ETL jobs and applications.

seq-pipeline - Workspace for data science projects and NGS pipelines. Contains RStudio, Jupyter Notebook, VSCode and file manager. Can connect to Tailscale network to bypass firewalls.

Dataplane - Dataplane is a data platform that makes it easy to construct a data mesh with automated data pipelines and workflows.

dagster - An orchestration platform for the development, production, and observation of data assets.

pipebird - Pipebird is open source infrastructure for securely sharing data with customers.

versatile-data-kit - One framework to develop, deploy and operate data workflows with Python and SQL.

hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.

AWS Data Wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

flowrunner - Flowrunner is a lightweight package to organize and represent Data Engineering/Science workflows

prism - Prism is the easiest way to develop, orchestrate, and execute data pipelines in Python.

SmartPipeline vs Mage patterns-devkit vs pyspark-example-project SmartPipeline vs seq-pipeline patterns-devkit vs Dataplane SmartPipeline vs dagster patterns-devkit vs pipebird SmartPipeline vs versatile-data-kit patterns-devkit vs hamilton patterns-devkit vs AWS Data Wrangler patterns-devkit vs flowrunner patterns-devkit vs prism patterns-devkit vs versatile-data-kit