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  5. Amazon Quicksight vs Denodo

Amazon Quicksight vs Denodo

OverviewComparisonAlternatives

Overview

Amazon Quicksight
Amazon Quicksight
Stacks207
Followers394
Votes5
Denodo
Denodo
Stacks40
Followers120
Votes0
GitHub Stars0
Forks0

Amazon Quicksight vs Denodo: What are the differences?

1. Scalability: Amazon Quicksight is a fully managed, serverless business intelligence service while Denodo is a data virtualization tool which provides dynamic data access. The difference is that Quicksight can scale automatically to handle large datasets and users, while Denodo's scalability depends on the underlying hardware and configurations set up by the user.

2. Integration: Amazon Quicksight seamlessly integrates with various AWS services such as S3, RDS, Redshift, etc., enabling quick data visualization and analysis from these sources. On the other hand, Denodo specializes in data virtualization, allowing integration of data from disparate sources without the need for data movement or replication.

3. Pricing Model: Amazon Quicksight offers pay-per-session and pay-per-user pricing models, providing flexibility based on business needs. In contrast, Denodo typically follows a more traditional licensing model based on cores, data sources, and users, which may not be as cost-effective for smaller organizations.

4. Performance Optimization: Quicksight provides in-memory calculation and SPICE (Super-fast, Parallel, In-memory, Calculation Engine) for fast query performance and data analysis. Denodo, on the other hand, focuses on virtualization and optimization of data access paths, which may not offer the same level of performance for complex analytical queries.

5. Data Sources Supported: While Amazon Quicksight is designed to work seamlessly with AWS data sources, Denodo supports a wide range of data sources including cloud databases, on-premise databases, web services, files, and APIs, making it more versatile in terms of data connectivity.

6. Customization Options: Quicksight offers limited customization options for dashboards and visualizations compared to Denodo, which allows for more extensive customization and fine-tuning of data presentation based on specific user requirements.

In Summary, Amazon Quicksight excels in scalability, integration with AWS services, and pricing flexibility, while Denodo stands out in data virtualization capabilities, performance optimization, and support for diverse data sources.

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Detailed Comparison

Amazon Quicksight
Amazon Quicksight
Denodo
Denodo

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 is the leader in data virtualization providing data access, data governance and data delivery capabilities across the broadest range of enterprise, cloud, big data, and unstructured data sources without moving the data from their original repositories.

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 virtualization; Data query; Data views
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
207
Stacks
40
Followers
394
Followers
120
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
DataRobot
DataRobot
AtScale
AtScale
Vertica
Vertica
Trifacta
Trifacta
Dremio
Dremio
Apache Kylin
Apache Kylin
SAP HANA
SAP HANA

What are some alternatives to Amazon Quicksight, Denodo?

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.

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.

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.

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