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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Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
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.
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Based on our record, Google Cloud Storage seems to be a lot more popular than Qrvey. While we know about 36 links to Google Cloud Storage, 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.
Cloud Storage: blog storage for static assets and media files. - Source: dev.to / 6 months ago
Preevy includes built-in support for saving profiles on AWS S3 and Google Cloud Storage. You can also store the profile on the local filesystem and copy it manually before running Preevy - we won't show this method here. - Source: dev.to / 6 months ago
Google Cloud Storage{:target="_blank"} is a globally distributed object storage service offered by Google Cloud Platform. They provide trustworthy and scalable databases for storing large amounts of blob data. They also provide a way to optimize cost and performance with different storage classes and pricing options. - Source: dev.to / 11 months ago
Google Cloud Storage - https://cloud.google.com/storage/. - Source: dev.to / 12 months ago
Also, in terms of packing a pre-trained model you will probably want to puts weights, biases etc into S3 or similar object storage (https://cloud.google.com/storage etc) and load it on application start. Source: about 1 year ago
Since you're on AWS already, check out https://qrvey.com. Source: 7 months ago
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