Qrvey VS Apache Mesos
Compare Qrvey VS Apache Mesos and see what are their differences
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Qrvey
DevicePilot
AnswerRocket
Syndigo
Amdocs CES
Yellowfin
Toucan Toco
Omniscope
Apache Mesos
Kubernetes
BOINC
Charity Engine
DIET by Avalon
Docker
GridRepublic
JPPF
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.
Qrvey Apache MesosNo features have been listed yet.
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 Mesos should be more popular than Qrvey. It has been mentiond 7 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.
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
When we adopted Kubernetes at Criteo, we encountered initial hurdles. In 2018, Kubernetes operators were still new, and there was internal competition from Mesos. We addressed these challenges by validating Kubernetes performance for our specific needs and building custom Chef recipes, StatefulSet hooks, and startup scripts. - Source: dev.to / about 1 month ago
In the beginning, there was docker. In 2013, building on linux internals, docker packaged containers for mass adoption and made it easy to share a complete runtime environment for an application across the network. Check out their first demo at PyCon 2013 (I was there!) At the time, serious workloads ran on something like Mesos, which was not “container-native” and had its own way of packaging and distributing... - Source: dev.to / 3 months ago
Distribution of containers to servers, clusters, and data centers Keeping applications up and running with the required number of instances Upgrading applications without downtime These issues are also known as cloud-native characteristics of modern applications. Therefore, a need for container orchestration systems has arisen. There are three leading container orchestrators on the market: Docker Swarm... - Source: dev.to / 10 months ago
Https://mesos.apache.org/ >Apache Mesos abstracts CPU, memory, storage, and other compute resources away from machines. - Source: Hacker News / over 1 year ago
Spark works locally on stand-alone clusters and on Hadoop YARN, Apache Mesos, Kubernetes, and other managed Hadoop platforms. - Source: dev.to / over 1 year ago
DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.
Kubernetes - Kubernetes is an open source orchestration system for Docker containers
AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.
BOINC - BOINC is an open-source software platform for computing using volunteered resources
Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.
Charity Engine - Charity Engine takes enormous, expensive computing jobs and chops them into 1000s of small pieces...