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, Amazon Managed Apache Cassandra Service should be more popular than Qrvey. It has been mentiond 2 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.
Amazon Keyspaces is a database service highly compatible with Apache Cassandra. It is a readily available service that can handle a high range of requests per second. Hence, Amazon Web Services (AWS) customers use Amazon Keyspaces (for Apache Cassandra) in order to modernize their Cassandra workloads. - Source: dev.to / over 1 year ago
Over time, we should have only dockers and lambda functions in our compute. While doing this, we should also discard the EC2 instances one by one and move onto Fargate. Drop the Kafka or other messaging services and move to Kinesis, EventBridge, SNS or SQS, as per the requirement. Migrate to cloud native databases like Aurora, DocumentDB, DynamoDB, and other purpose built databases like TimeStream, Keyspace,... - Source: dev.to / almost 3 years ago
Since you're on AWS already, check out https://qrvey.com. Source: 6 months ago
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