patterns-devkit VS prism

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

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patterns-devkit prism
5 7
106 79
0.0% -
2.9 8.9
about 1 year ago about 1 month ago
Python Python
BSD 3-clause "New" or "Revised" License Apache License 2.0
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.

prism

Posts with mentions or reviews of prism. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-27.

What are some alternatives?

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

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

datavault4dbt - Scalefree's dbt package for a Data Vault 2.0 implementation congruent to the original Data Vault 2.0 definition by Dan Linstedt including the Staging Area, DV2.0 main entities, PITs and Snapshot Tables.

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

JDR - Job Dependency Runner

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

retake - PostgreSQL for Search [Moved to: https://github.com/paradedb/paradedb]

SmartPipeline - A framework for rapid development of robust data pipelines following a simple design pattern

paradedb - Postgres for Search and Analytics

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

data-diff - Compare tables within or across databases

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).

jupysql - Better SQL in Jupyter. 📊