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  5. Periscope vs Sisense

Periscope vs Sisense

OverviewComparisonAlternatives

Overview

Periscope
Periscope
Stacks45
Followers94
Votes10
Sisense
Sisense
Stacks50
Followers66
Votes0

Periscope vs Sisense: What are the differences?

Introduction

When comparing Periscope and Sisense, it's essential to understand the key differences between these two powerful data visualization tools.

  1. Deployment Options: Periscope is a cloud-based solution, meaning it provides ease of accessibility and scalability without the need for extensive IT infrastructure. On the other hand, Sisense offers on-premise, cloud, and hybrid deployment options, giving users more flexibility in choosing where to store and access their data.

  2. User Interface and Ease of Use: Periscope is known for its user-friendly interface and intuitive design, making it easy for users of all levels to create and share insights. Sisense, on the other hand, offers a more robust set of features that may require a steeper learning curve but ultimately provides more advanced customization options and capabilities.

  3. Integration Capabilities: Periscope integrates seamlessly with popular data sources such as Amazon Redshift, Google BigQuery, and PostgreSQL, making it easy to connect and visualize data from various platforms. Sisense, on the other hand, offers a wide range of connectors and APIs, allowing for more extensive integration with third-party tools and services.

  4. Scalability and Performance: Periscope is well-suited for small to medium-sized businesses looking for a quick and easy way to visualize their data. In contrast, Sisense is designed to handle large and complex datasets, making it a preferred choice for enterprises with massive data requirements and processing needs.

  5. Pricing Structure: Periscope offers a straightforward pricing model based on the number of users, making it easy to budget for and scale as needed. Sisense, on the other hand, follows a more customized pricing approach, with costs varying depending on factors such as data volume, concurrent users, and specific feature requirements.

  6. Collaboration and Sharing Features: Periscope excels in collaboration features, allowing users to easily share dashboards, reports, and insights with team members and stakeholders. In comparison, Sisense offers robust sharing capabilities with advanced security settings and permissions control, making it ideal for organizations with strict data governance requirements.

In Summary, Periscope and Sisense differ in deployment options, user interface, integration capabilities, scalability, pricing structure, and collaboration features, catering to a range of user needs and preferences.

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Detailed Comparison

Periscope
Periscope
Sisense
Sisense

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

It is making business intelligence (BI) analytics easy with its simple drag-and-drop and scalable end-to-end BI processes that help to prepare, analyze, and visualize multiple complex datasets quickly.

Beautiful Visualizations;Expert Query Tools;Seamless Sharing
Complex business queries without programming or SQL writing; Removes limitations to data size; Intuitive 'script-less' user interface; Consolidates, stores and accumulates data; Data mash-ups; Can be set up and used entirely in-house; Handles data at scale on a single commodity server.
Statistics
Stacks
45
Stacks
50
Followers
94
Followers
66
Votes
10
Votes
0
Pros & Cons
Pros
  • 6
    Great for learning and teaching people SQL
  • 4
    Gorgeous "share-able" and "embeddable" dashboards
No community feedback yet
Integrations
No integrations available
AtScale
AtScale
Vertica
Vertica
Dremio
Dremio
DataRobot
DataRobot
Trifacta
Trifacta
SAP HANA
SAP HANA

What are some alternatives to Periscope, Sisense?

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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