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  5. Cumul.io vs Microsoft SSRS

Cumul.io vs Microsoft SSRS

OverviewComparisonAlternatives

Overview

Microsoft SSRS
Microsoft SSRS
Stacks96
Followers138
Votes0
Cumul.io
Cumul.io
Stacks7
Followers15
Votes0

Cumul.io vs Microsoft SSRS: What are the differences?

Introduction:

Microsoft SQL Server Reporting Services (SSRS) and Cumul.io are two popular business intelligence tools that allow users to create and share interactive reports and dashboards. However, these tools have key differences that make them suitable for different use cases.

  1. Deployment and Hosting: Cumul.io is a cloud-based business intelligence platform, which means users can access their reports and dashboards from anywhere with an internet connection. On the other hand, Microsoft SSRS is typically deployed on-premises, requiring users to have access to the network where the SSRS server is hosted.

  2. Data Sources: Cumul.io supports a wide range of data sources, including cloud applications, databases, and APIs, making it easy for users to connect to different data sources. In contrast, Microsoft SSRS is primarily designed to work with Microsoft SQL Server databases, which may limit its ability to connect to other data sources.

  3. Ease of Use: Cumul.io is known for its user-friendly interface and drag-and-drop functionality, allowing users to create visualizations and reports quickly without the need for extensive technical knowledge. On the other hand, Microsoft SSRS has a steeper learning curve and may require users to have a strong understanding of SQL and report design concepts.

  4. Collaboration and Sharing: Cumul.io enables seamless collaboration among team members, allowing multiple users to work on the same report or dashboard simultaneously. Users can also easily share reports with others through secure links. In comparison, Microsoft SSRS is better suited for individual report development and distribution, with limited collaboration features.

  5. Customization and Extensibility: Cumul.io offers a wide range of customization options, including the ability to create custom themes, integrate with third-party applications, and embed reports into external websites. Microsoft SSRS, while customizable, may have limitations in terms of extensibility and integration with other tools and platforms.

  6. Cost: Cumul.io offers subscription-based pricing plans that scale based on the number of users and features required, making it a more cost-effective option for small to medium-sized businesses. Microsoft SSRS, on the other hand, is often included as part of the SQL Server licensing package, but may require additional costs for deployment, maintenance, and support.

In summary, Cumul.io and Microsoft SSRS have key differences in terms of deployment, data sources, ease of use, collaboration, customization, and cost, making them suitable for different business intelligence needs.

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Detailed Comparison

Microsoft SSRS
Microsoft SSRS
Cumul.io
Cumul.io

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.

Create powerful dashboards in minutes. Share with your teams, or integrate in your platform. Drive growth in your business today!

"Traditional" paginated reports; New mobile reports; A modern web portal
-
Statistics
Stacks
96
Stacks
7
Followers
138
Followers
15
Votes
0
Votes
0
Integrations
Microsoft SQL Server
Microsoft SQL Server
Google Drive
Google Drive
MongoDB
MongoDB
PostgreSQL
PostgreSQL
MariaDB
MariaDB
Clickhouse
Clickhouse
Amazon Redshift
Amazon Redshift
PipelineDB
PipelineDB
Amazon Athena
Amazon Athena
Microsoft Excel
Microsoft Excel
Microsoft SQL Server
Microsoft SQL Server

What are some alternatives to Microsoft SSRS, Cumul.io?

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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