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  1. Stackups
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  5. Incorta vs Mineo

Incorta vs Mineo

OverviewComparisonAlternatives

Overview

Incorta
Incorta
Stacks2
Followers4
Votes0
Mineo
Mineo
Stacks0
Followers1
Votes0

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Detailed Comparison

Incorta
Incorta
Mineo
Mineo

It is a next-generation data analytics and business intelligence platform that excels at rapidly delivering business value from transactional data and is the first real breakthrough in data analytics in 20 years. It provides an integrated end-to-end data experience, from data acquisition and enrichment to visualizing and sharing results. It cuts project implementation time from months to weeks, provides revolutionary query speed, and maintains a unified, single-source of truth for multiple workloads including business intelligence, analytics, and machine learning.

It is the platform to explore your data, develop and deploy your Python supercharged Notebooks and track the quality of your data using Machine learning.

Extensible connector architecture; Parallel data loading; Direct data mapping; Embedded notebooks; Machine learning; Spark; Business views; Materialized views; Enterprise security; Query editor; Dashboards; 3rd party tool support; REST API; Parquet data lake; Blueprints
Develop, share and deploy your supercharged Python notebooks on a scalable platform; Automate and streamline your workflows with Python-based data pipelines that leverage the power of notebooks; Establish processes to measure and track the quality of your data over the time using machine learning
Statistics
Stacks
2
Stacks
0
Followers
4
Followers
1
Votes
0
Votes
0
Integrations
Tableau
Tableau
Power BI
Power BI
Microsoft Excel
Microsoft Excel
PingFederate
PingFederate
Microsoft SQL Server
Microsoft SQL Server
Python
Python
PostgreSQL
PostgreSQL
Amazon Redshift
Amazon Redshift
Clickhouse
Clickhouse
Oracle
Oracle
Slack
Slack
OpenID Connect
OpenID Connect

What are some alternatives to Incorta, Mineo?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Jupyter

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Apache Zeppelin

Apache Zeppelin

A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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.

Mode

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

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.

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