Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application.
Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less.
Qrvey’s multi-tenant data lake includes:
Qrvey’s embedded visualizations support everything from: - Standard dashboards and templates - Self-service reporting - User-level personalization - Individual dataset creation - Data-driven workflow automation
Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users.
The result: Less time and money on analytics.
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Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
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Qrvey takes a different approach to embedded analytics. Instead of focusing almost completely on the front end, we know that any analytics function starts with data.
Qrvey includes a full-featured data lake powered by Elasticsearch, not a basic relational caching layer. Furthermore, by including a data lake, the cost to scale out is much less than traditional data warehouses.
For the user-facing components of the platform, Qrvey offers more embedded components and APIs to personalize the experience beyond static dashboards. Qrvey offers:
All of this is backed by a semantic layer that makes integrating Qrvey into the security model of SaaS applications simple.
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Customers choose Qrvey for the following reasons:
Based on our record, Guava seems to be a lot more popular than Qrvey. While we know about 19 links to Guava, we've tracked only 1 mention of Qrvey. 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: 6 months ago
As a racist against Java Regex, I can't help wanting to try it out without regex (using Guava and Mug, my all-in-one toolbox). - Source: dev.to / about 1 month ago
The first problem is at the level of Type System, given that a situation more correct would allow us to distinguish through the Collection Type which abstraction we are operating with, species if definable as mutable or immutable. The JCF was born at a time when great care was taken to offer immediate operational data structures, and with attention to performance, but with less attention to constructs or uses... - Source: dev.to / 6 months ago
Java Mask: Java Mask is a library that offers various string masking techniques for sensitive data such as credit card numbers, email addresses, and more. You can find the library at: https://github.com/miguelfreitas93/java-mask DataMasker: DataMasker is a Java library specifically designed for masking sensitive data, including credit card numbers, using customizable masking patterns. Visit the GitHub repository... Source: about 1 year ago
I mean, I guess, technically? If you define it like that, then Microsoft has people working for them for free, as does Google, as does Apple, etc. It's not that weird, and you can try to twist it to be weird, but those of us in the software industry largely regard this as a good thing. Source: about 1 year ago
Ok, so let's implement our lazy initialization more smartly to avoid acquiring the lock every time and use old fashion but still working double-checked locking. I've found it implemented by Suppliers.memoize in guava library. - Source: dev.to / over 1 year ago
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