Resque
Sidekiq
Hangfire
delayed_job
Enqueue It
RabbitMQ
Apache Kafka
Amazon SQS
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, Resque should be more popular than Qrvey. It has been mentiond 5 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.
You can use a background job queue like Resque to scrape and process data in the background, and a scheduler like resque-scheduler to schedule jobs to run your scraper periodically. Source: almost 2 years ago
So how do we trigger such a long-running process from a Rails request? The first option that comes to mind is a background job run by some of the queuing back-ends such as Sidekiq, Resque or DelayedJob, possibly governed by ActiveJob. While this would surely work, the problem with all these solutions is that they usually have a limited number of workers available on the server and we didn’t want to potentially... - Source: dev.to / about 2 years ago
Background jobs are another limitation. Since only the Aha! Web service runs in a dynamic staging, the host environment's workers would process any Resque jobs that were sent to the shared Redis instance. If your branch hadn't updated any background-able methods, this would be no big deal. But if you were hoping to test changes to these methods, you would be out of luck. - Source: dev.to / about 2 years ago
The Schedules worker corresponds to the appwrite-schedule service in the docker-compose file. The Schedules worker uses a Resque Scheduler under the hood and handles the scheduling of CRON jobs across Appwrite. This includes CRON jobs from the Tasks API, Webhooks API, and the functions API. - Source: dev.to / almost 3 years ago
There are a few of popular systems. A few need a database, such as Delayed::Job, while others prefer Redis, such as Resque and Sidekiq. - Source: dev.to / about 3 years ago
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
Sidekiq - Sidekiq is a simple, efficient framework for background job processing in Ruby
DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.
Hangfire - An easy way to perform background processing in .NET and .NET Core applications.
AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.
delayed_job - Database based asynchronous priority queue system -- Extracted from Shopify - collectiveidea/delayed_job
Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.
