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  5. i-DOCS vs Viewflow

i-DOCS vs Viewflow

OverviewComparisonAlternatives

Overview

i-DOCS
i-DOCS
Stacks1
Followers0
Votes0
Viewflow
Viewflow
Stacks1
Followers8
Votes0
GitHub Stars125
Forks11

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CLI (Node.js)
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Detailed Comparison

i-DOCS
i-DOCS
Viewflow
Viewflow

It is a leading provider in the specialized market of Enterprise Output Management. i-DOCS develops products and offers services that handle big volumes of sensitive data, automate business processes, deliver multi-channel communications, serve, store, archive data and documents.

It is a framework built on the top of Airflow that enables data scientists to create materialized views. It allows data scientists to focus on the logic of the view creation in their preferred tool (e.g., SQL, Python).

ETL (extract transform load) services; Third party integration services; Output management- migration services; Output management -document design/redesign services; Big data & business analytics
Airflow-based framework; Allows data scientists to create data models without writing Airflow code
Statistics
GitHub Stars
-
GitHub Stars
125
GitHub Forks
-
GitHub Forks
11
Stacks
1
Stacks
1
Followers
0
Followers
8
Votes
0
Votes
0
Integrations
No integrations available
Airflow
Airflow
Python
Python

What are some alternatives to i-DOCS, Viewflow?

Pandas

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

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

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!

Welcome to Baselight Assistant

Welcome to Baselight Assistant

Baselight unlocks the power of data, combining openness, community, and AI to make high-quality structured data accessible to all.

CBDC Resources

CBDC Resources

CBDC Resources is a data and analytics platform that centralizes global information on Central Bank Digital Currency (CBDC) projects. It provides structured datasets, interactive visualizations, and technology-oriented insights used by fintech developers, analysts, and research teams. The platform aggregates official documents, technical specifications, and implementation details from institutions such as the IMF, BIS, ECB, and national central banks. Developers and product teams use CBDC Resources to integrate CBDC data into research workflows, dashboards, risk models, and fintech applications. Website : https://cbdcresources.com/

SciPy

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

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

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

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

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.

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