Qrvey Analytics vs Tableau

Overview

Tableau

Stacks1.3K

Followers1.4K

Votes8

Qrvey Analytics

Stacks2

Followers4

Votes0

Qrvey Analytics vs Tableau: What are the differences?

Data Visualization Capabilities: One key difference between Qrvey Analytics and Tableau is that Qrvey offers more advanced data visualization tools, allowing users to create interactive and dynamic charts, graphs, and dashboards quickly and easily. On the other hand, Tableau provides a wide array of visualization options and customization features, giving users more control over the design and layout of their visualizations.
Data Integration Options: Qrvey Analytics is known for its seamless integration with popular data sources, including databases, flat files, and web services, making it easy for users to connect to and analyze their data. In contrast, Tableau also offers a variety of data connection options but may require additional plugins or configurations to connect to certain data sources.
Collaboration and Sharing Features: When it comes to collaboration and sharing, Qrvey Analytics allows users to share reports and dashboards with other team members in real-time, facilitating collaboration and decision-making. Tableau, on the other hand, offers similar sharing capabilities but may require users to publish their visualizations to Tableau Server or Tableau Online for broader access.
Ease of Use: Qrvey Analytics is designed with simplicity in mind, offering a user-friendly interface that makes it easy for users to create and analyze data without the need for extensive training or technical expertise. In contrast, Tableau provides a more robust platform with advanced features that may require a steeper learning curve for new users.
Cost Structure: When it comes to pricing, Qrvey Analytics offers a more straightforward and flexible pricing model, allowing users to pay for only the features they need. Tableau, on the other hand, offers different pricing tiers based on the features and functionality users require, which can result in higher costs for some organizations.
Scalability and Performance: Qrvey Analytics is designed to be highly scalable, allowing users to handle large volumes of data and complex analytics queries with ease. Tableau also offers scalability features but may require additional resources or configurations to maintain optimal performance for larger data sets and more complex analyses.

In Summary, Qrvey Analytics and Tableau differ in their data visualization capabilities, data integration options, collaboration and sharing features, ease of use, cost structure, and scalability and performance.

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Advice on Tableau, Qrvey Analytics

Vojtech

Head of Data at Mews

Nov 24, 2019

Decided

Power BI is really easy to start with. If you have just several Excel sheets or CSV files, or you build your first automated pipeline, it is actually quite intuitive to build your first reports.

And as we have kept growing, all the additional features and tools were just there within the Azure platform and/or Office 365.

Since we started building Mews, we have already passed several milestones in becoming start up, later also a scale up company and now getting ready to grow even further, and during all these phases Power BI was just the right tool for us.

353k views353k

Comments

Wei

CTO at Flux Work

Jan 8, 2020

Decided

Very easy-to-use UI. Good way to make data available inside the company for analysis.

Has some built-in visualizations and can be easily integrated with other JS visualization libraries such as D3.

Can be embedded into product to provide reporting functions.

Support team are helpful.

The only complain I have is lack of API support. Hard to track changes as codes and automate report deployment.

230k views230k

Comments

Detailed Comparison

Tableau	Qrvey Analytics
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.	It makes analytics on AWS easy with an all-in-one platform that includes data collection, transformation, analysis, visualizations, automation and machine learning. It's perfect for distributed and embedded use cases for the enterprise and software companies.
Connect to data on prem or in the cloud—whether it’s big data, a SQL database, a spreadsheet, or cloud apps like Google Analytics and Salesforce. Access and combine disparate data without writing code. Power users can pivot, split, and manage metadata to optimize data sources. Analysis begins with data. Get more from yours with Tableau.; Exceptional analytics demand more than a pretty dashboard. Quickly build powerful calculations from existing data, drag and drop reference lines and forecasts, and review statistical summaries. Make your point with trend analyses, regressions, and correlations for tried and true statistical understanding. Ask new questions, spot trends, identify opportunities, and make data-driven decisions with confidence.; Answer the “where” as well as the “why.” Create interactive maps automatically. Built-in postal codes mean lightning-fast mapping for more than 50 countries worldwide. Use custom geocodes and territories for personalized regions, like sales areas. We designed Tableau maps specifically to help your data stand out.; Ditch the static slides for live stories that others can explore. Create a compelling narrative that empowers everyone you work with to ask their own questions, analyzing interactive visualizations with fresh data. Be part of a culture of data collaboration, extending the impact of your insights.	Data collection;Data transformation;Data visualization;Data automation; Machine learning:charts, reports, metrics and dashboards
Statistics
Stacks 1.3K	Stacks 2
Followers 1.4K	Followers 4
Votes 8	Votes 0
Pros & Cons
Pros 6 Capable of visualising billions of rows 1 Responsive 1 Intuitive and easy to learn Cons 3 Very expensive for small companies	No community feedback yet

What are some alternatives to Tableau, Qrvey Analytics?

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.

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.

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

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.

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

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.

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Shiny

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Qrvey Analytics vs Tableau: What are the differences?

Data Visualization Capabilities: One key difference between Qrvey Analytics and Tableau is that Qrvey offers more advanced data visualization tools, allowing users to create interactive and dynamic charts, graphs, and dashboards quickly and easily. On the other hand, Tableau provides a wide array of visualization options and customization features, giving users more control over the design and layout of their visualizations.
Data Integration Options: Qrvey Analytics is known for its seamless integration with popular data sources, including databases, flat files, and web services, making it easy for users to connect to and analyze their data. In contrast, Tableau also offers a variety of data connection options but may require additional plugins or configurations to connect to certain data sources.
Collaboration and Sharing Features: When it comes to collaboration and sharing, Qrvey Analytics allows users to share reports and dashboards with other team members in real-time, facilitating collaboration and decision-making. Tableau, on the other hand, offers similar sharing capabilities but may require users to publish their visualizations to Tableau Server or Tableau Online for broader access.
Ease of Use: Qrvey Analytics is designed with simplicity in mind, offering a user-friendly interface that makes it easy for users to create and analyze data without the need for extensive training or technical expertise. In contrast, Tableau provides a more robust platform with advanced features that may require a steeper learning curve for new users.
Cost Structure: When it comes to pricing, Qrvey Analytics offers a more straightforward and flexible pricing model, allowing users to pay for only the features they need. Tableau, on the other hand, offers different pricing tiers based on the features and functionality users require, which can result in higher costs for some organizations.
Scalability and Performance: Qrvey Analytics is designed to be highly scalable, allowing users to handle large volumes of data and complex analytics queries with ease. Tableau also offers scalability features but may require additional resources or configurations to maintain optimal performance for larger data sets and more complex analyses.

Qrvey Analytics vs Tableau

Overview