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, DynamoDB seems to be a lot more popular than Qrvey. While we know about 104 links to DynamoDB, 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: 7 months ago
DynamoDB is a powerful NoSQL database provided by AWS, designed to handle large amounts of data efficiently. However, for newcomers, understanding the nuances of querying DynamoDB tables can be challenging, particularly when it comes to the differences between KeyConditionExpression and FilterExpression. This blog post aims to clarify these concepts and provide practical examples of their usage. - Source: dev.to / about 1 month ago
Event Producers: Generate streams of events, which can be implemented using straightforward microservices with AWS Lambda (for serverless computing), Amazon DynamoDB Streams (to captures changes to DynamoDB tables in real-time), Amazon S3 Event Notifications (Notify when certain events occur in S3 buckets) or AWS Fargate (a serverless compute engine for containers). - Source: dev.to / about 2 months ago
The first is AWS DynamoDB which is going to act as our NoSQL database for our project which we’re also going to pair with a Single-Table design architecture. - Source: dev.to / about 2 months ago
DynamoDB - 25GB NoSQL DB EC2 - 750 hours per month of t2.micro or t3.micro(12mo). 100GB egress per month. - Source: dev.to / 5 months ago
After two years, I moved to a Web3 startup where I was given a lead software engineer role. This new role gave me more hands-on experience with AWS, where I've learned to implement serverless technologies like Lambda and DynamoDB. - Source: dev.to / 6 months ago
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