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  5. Amazon Quicksight vs Qrvey Analytics

Amazon Quicksight vs Qrvey Analytics

OverviewComparisonAlternatives

Overview

Amazon Quicksight
Amazon Quicksight
Stacks207
Followers394
Votes5
Qrvey Analytics
Qrvey Analytics
Stacks2
Followers4
Votes0

Amazon Quicksight vs Qrvey Analytics: What are the differences?

<Amazon Quicksight vs Qrvey Analytics>
  1. Data Sources: Amazon QuickSight offers integrations with a wide range of data sources such as Amazon Redshift, RDS, SQL Server, Salesforce, and more, while Qrvey Analytics focuses on cloud-native data sources, including MongoDB, Amazon DynamoDB, and more.

  2. Scalability: Amazon QuickSight's scalability is ideal for medium to large enterprises dealing with massive datasets and complex analytics requirements, whereas Qrvey Analytics is more suited for small to medium businesses with simpler analytics needs and smaller datasets.

  3. Machine Learning Capabilities: Amazon QuickSight provides robust machine learning capabilities for predictive analytics, anomaly detection, and natural language queries, while Qrvey Analytics focuses more on traditional business intelligence reporting and dashboarding without advanced ML integration.

  4. Deployment Options: Amazon QuickSight offers both cloud-based and on-premise deployment options, providing flexibility for different organizational needs, while Qrvey Analytics is solely cloud-based, limiting deployment options for businesses requiring on-premise solutions.

  5. Collaboration Features: Amazon QuickSight provides extensive collaboration features like real-time sharing, commenting, and user permissions for team collaboration on dashboards and reports, whereas Qrvey Analytics offers more basic collaboration features, limiting real-time interactions and collaborative capabilities.

  6. Customization and White Labeling: Amazon QuickSight allows for extensive customization and white-labeling of dashboards and reports, enabling branding according to user requirements, while Qrvey Analytics has limited customization options, restricting branding capabilities for organizations looking for a tailored user experience.

In Summary, Amazon QuickSight is a robust and scalable analytics platform with advanced machine learning capabilities suitable for large enterprises, while Qrvey Analytics is a cloud-native solution tailored for small to medium businesses with simpler analytics requirements.

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Detailed Comparison

Amazon Quicksight
Amazon Quicksight
Qrvey Analytics
Qrvey Analytics

Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data.

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.

Pay-per-session pricing; Deliver rich, interactive dashboards for your readers; Explore, analyze, collaborate; SPICE (super-fast, parallel, in-memory, calculation engine); ML Insights
Data collection;Data transformation;Data visualization;Data automation; Machine learning:charts, reports, metrics and dashboards
Statistics
Stacks
207
Stacks
2
Followers
394
Followers
4
Votes
5
Votes
0
Pros & Cons
Pros
  • 1
    Super cheap
  • 1
    Better integration with aws
  • 1
    More features (table calculations, functions, insights)
  • 1
    Good integration with aws Glue ETL services
  • 1
    Dataset versionning
Cons
  • 1
    Very basic BI tool
  • 1
    Only works in AWS environments (not GCP, Azure)
No community feedback yet
Integrations
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Amazon Aurora
Amazon Aurora
Amazon Redshift
Amazon Redshift
No integrations available

What are some alternatives to Amazon Quicksight, Qrvey Analytics?

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.

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.

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.

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.

AskNed

AskNed

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

Shiny

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

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

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.

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