Dataiku vs Pretzel

Overview

Dataiku

Stacks33

Followers27

Votes0

Pretzel

Stacks0

Followers2

Votes0

GitHub Stars2.2K

Forks159

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Detailed Comparison

Dataiku	Pretzel
It is the platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way.	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.
Spend more time on high-impact AI projects; The power of AI + your business expertise = unlimited opportunity; Leverage the power of AI in every business decision for transformative impact	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 33	Stacks 0
Followers 27	Followers 2
Votes 0	Votes 0
Integrations
No integrations available	Render DuckDB PRQL

What are some alternatives to Dataiku, 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

Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

SciPy

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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.