Apache TinkerPop
neo4j
JanusGraph
ArangoDB
GrapheneDB
MongoDB
OrientDB
Blazegraph
Qrvey
DevicePilot
AnswerRocket
Syndigo
Amdocs CES
Yellowfin
Toucan Toco
Omniscope
Designed for external use cases where SaaS companies need to provide their customers with powerful and customizable analytics capabilities.
Qrvey is the only full stack solution that offers all the embedded visualization and self-service analytics tools along with a unified data pipeline that offers a data lake optimized for multi-tenant analytics.
Qrvey's embedded visualizations empower engineering teams to build custom experiences, along with full white labeling and CSS customization options to make Qrvey’s javascript widgets blend seamlessly into a SaaS application. ⋅⋅* Qrvey’s data-driven automation workflows enable the creation of complex workflows based on data triggers, such as conditional logic, nested functions, data write-backs with notification integrations to third party systems such as Slack. ⋅⋅* Qrvey supports natural language querying of data using generative AI to easily spot trends and outliers, augmented analysis capabilities. ⋅⋅* Qrvey also supports pixel perfect reporting to generate printable reports from the same analytics data.
Qrvey simplifies data management by providing a single data pipeline solution featuring a data lake solution that is optimized for multi-tenant analytics. This contains native data connectors and APIs to ingest data in any type from any source, including real-time data with live connections. ⋅⋅* Qrvey’s semantic layer can inherit and map security models from your multi-tenant SaaS application, saving software development teams the hassle of duplicating users and roles. ⋅⋅* Qrvey’s robust API allows you to create data delivery services and managed download functions that go beyond basic exporting.
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Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Qrvey's answer:
Qrvey's approach to embedded analytics is different. Qrvey combines the best of BI, data warehousing, and data visualization into a single solution built exclusively for SaaS applications.
Qrvey's key features include:
100% Embeddability - Everything is embeddable with JS based components that supports full white labeling so you can create unique analytics experiences within your SaaS application.
Data Warehouse included - Visualizations are useless without a scalable data layer built specifically for analytics use cases. Qrvey includes native multi-tenant support so your data is ready for your multi-tenant SaaS application. This includes data syncing and API support that allows for any type of data to be ingested into the Qrvey data layer.
Self-Hosted - Deployed to Your AWS Environment. Customers get ultimate control as Qrvey is deployed to their AWS environment inheriting and respecting their security policies. Your data never leaves, but it's ready for analytics now.
Based on our record, Apache TinkerPop should be more popular than Qrvey. It has been mentiond 4 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The API for Gremlin is built based on Apache TinkerPop, a graph computing framework that uses the Gremlin query language. - Source: dev.to / 6 days ago
You might take a look at Tinkerpop: https://tinkerpop.apache.org/. - Source: Hacker News / 2 months ago
Property Graph, mainly represented as node and relationship in which they can have properties. The database for this kind of data is usually called Graph Database. Gremlin - by TinkerPop project and Cypher - by Neo4J are their query language (also AQL - Arango Query Language - by ArangoDB, but AQL does not only provides graph query language). - Source: dev.to / over 2 years ago
The most common graph query language at the moment would be Gremlin, which is part of the Apache TinkePop graph computing framework. It is simple to write, easy to learn, and widely supported by many graph databases and even non-graph databases that can emulate graph queries. On the other hand, it can be verbose for long queries but generally works well for both OLTP and analysis work. - Source: dev.to / almost 3 years ago
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.
DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.
JanusGraph - JanusGraph is a scalable graph database optimized for storing and querying graphs.
AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.
