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  5. Amazon Quicksight vs Microsoft SQL Server Reporting Services

Amazon Quicksight vs Microsoft SQL Server Reporting Services

OverviewComparisonAlternatives

Overview

Microsoft SQL Server Reporting Services
Microsoft SQL Server Reporting Services
Stacks44
Followers30
Votes0
Amazon Quicksight
Amazon Quicksight
Stacks207
Followers394
Votes5

Amazon Quicksight vs Microsoft SQL Server Reporting Services: What are the differences?

# Introduction

1. **Pricing Model**: Amazon Quicksight offers a pay-per-session pricing model, meaning users only pay for the number of sessions run, while Microsoft SQL Server Reporting Services requires a per-server or per-core licensing which can become expensive for organizations with large user bases.
  
2. **Data Sources**: Amazon Quicksight has native integration with various data sources like AWS services and SaaS applications, providing seamless connectivity, whereas Microsoft SQL Server Reporting Services primarily works with SQL Server data sources and requires additional setup for integration with other sources.
  
3. **Collaboration and Sharing**: Amazon Quicksight allows easy sharing of interactive dashboards through URLs or embedded visuals in other applications, promoting collaboration across teams, compared to Microsoft SQL Server Reporting Services which focuses more on traditional static reports with limited sharing capabilities.
  
4. **Scalability**: Amazon Quicksight is a cloud-based solution allowing for easy scalability based on user demand, while Microsoft SQL Server Reporting Services requires additional infrastructure and licensing costs for scaling up as the user base grows.
  
5. **Customization Options**: Microsoft SQL Server Reporting Services provides more advanced customization options and greater control over report formatting and layout compared to the more user-friendly and template-driven approach of Amazon Quicksight.
  
6. **Mobile Support**: Amazon Quicksight offers responsive and optimized mobile support, allowing users to access and interact with reports on various devices easily, while Microsoft SQL Server Reporting Services may require additional configurations for mobile compatibility and responsiveness.

In Summary, Amazon Quicksight and Microsoft SQL Server Reporting Services differ in their pricing model, data sources, collaboration features, scalability, customization options, and mobile support capabilities, catering to different user needs and preferences.

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Detailed Comparison

Microsoft SQL Server Reporting Services
Microsoft SQL Server Reporting Services
Amazon Quicksight
Amazon Quicksight

It provides a set of on-premises tools and services that create, deploy, and manage mobile and paginated reports. It delivers the right information to the right users.

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.

"Traditional" paginated reports; New mobile reports; A modern web portal
Pay-per-session pricing; Deliver rich, interactive dashboards for your readers; Explore, analyze, collaborate; SPICE (super-fast, parallel, in-memory, calculation engine); ML Insights
Statistics
Stacks
44
Stacks
207
Followers
30
Followers
394
Votes
0
Votes
5
Pros & Cons
No community feedback yet
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)
Integrations
Microsoft SQL Server
Microsoft SQL Server
Amazon RDS
Amazon RDS
Amazon S3
Amazon S3
Amazon Aurora
Amazon Aurora
Amazon Redshift
Amazon Redshift

What are some alternatives to Microsoft SQL Server Reporting Services, Amazon Quicksight?

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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