patterns-devkit VS SmartPipeline

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

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patterns-devkit SmartPipeline
5 1
106 22
0.0% -
2.9 7.5
about 1 year ago 2 months ago
Python Python
BSD 3-clause "New" or "Revised" License GNU Lesser General Public License v3.0 only
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.

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.

SmartPipeline

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

What are some alternatives?

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

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

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

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

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.

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

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

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

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

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.