erwin Data Modeler vs Mito

Overview

erwin Data Modeler

Stacks6

Followers3

Votes0

Mito

Stacks0

Followers3

Votes0

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Detailed Comparison

erwin Data Modeler	Mito
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 a spreadsheet that lives inside your JupyterLab notebooks. It allows you to edit Pandas dataframes like an Excel file, and generates Python code that corresponds to each of your edits.
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	Point-and-click CSV and XLSX import; Excel-style pivot tables; Graph generation; Filtering and sorting; Merge (lookups); Excel-Style formulas; Column summary statistics
Statistics
Stacks 6	Stacks 0
Followers 3	Followers 3
Votes 0	Votes 0
Integrations
No integrations available	Python Jupyter

What are some alternatives to erwin Data Modeler, Mito?

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.