erwin Data Modeler vs Pretzel

Overview

erwin Data Modeler

Stacks6

Followers3

Votes0

Pretzel

Stacks0

Followers2

Votes0

GitHub Stars2.2K

Forks159

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Detailed Comparison

erwin Data Modeler	Pretzel
It is data modeling tool used to find, visualize, design, deploy and standardize high-quality enterprise data assets. Discover and document any data from anywhere for consistency, clarity and artifact reuse across large-scale data integration, master data management, metadata management, Big Data, business intelligence and analytics initiatives – all while supporting data governance and intelligence efforts.	It is an open-source, offline browser-based tool for fast and intuitive data exploration and visualization. It can handle large data files, runs locally in your browser, and requires no backend setup.
Visualization of Any Data From Anywhere; Automated Data Model & Database Schema Generation; Centralized Model Development & Management; Data Literacy, Collaboration & Accountability; Agile Application Development; Increased Data Quality	Blazing-fast performance with WebAssembly-based DuckDB and PRQL; AI-powered transformation block to help with fast data manipulation; Intuitive data exploration with a visual, top-down pipeline of data transformations and visualizations; Run Pretzel AI locally or host it yourself for full control over your data
Statistics
GitHub Stars -	GitHub Stars 2.2K
GitHub Forks -	GitHub Forks 159
Stacks 6	Stacks 0
Followers 3	Followers 2
Votes 0	Votes 0
Integrations
No integrations available	Render DuckDB PRQL

What are some alternatives to erwin Data Modeler, Pretzel?

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

NumPy

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PyXLL

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SciPy

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Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

PySpark

It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.

Anaconda

A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.

Dask

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

Pentaho Data Integration

It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.