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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Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
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Customers choose Qrvey for the following reasons:
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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, Apache Calcite seems to be a lot more popular than Qrvey. While we know about 12 links to Apache Calcite, 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
> Make diff work on more than just SQLite. Another way of doing this that I've been wanting to do for a while is to implement the DIFF operator in Apache Calcite[0]. Using Calcite, DIFF could be implemented as rewrite rules to generate the appropriate SQL to be directly executed against the database or the DIFF operator can be implemented outside of the database (which the original paper shows is more efficient).... - Source: Hacker News / 10 months ago
Use a SQL Parser like sqlglot or Apache Calcite to compile user's query into an AST. Source: about 1 year ago
One parser I think deserves a mention is the one from Apache Calcite[0]. Calcite does more than parsing, there are a number of users who pick up Calcite just for the parser. While the default parser attempts to adhere strictly to the SQL standard, of interest is also the Babel parser, which aims to be as permissive as possible in accepting different dialects of SQL. Disclaimer: I am on the PMC of Apache Calcite,... - Source: Hacker News / over 1 year ago
Apache Calcite can do this, though it's not a beginner-friendly task: https://calcite.apache.org/. - Source: Hacker News / almost 2 years ago
You should look at Apache Calcite[0]. Like OctoSQL, you can join data from different data sources. It's also relatively easy to add your own data sources ("adapters" in Calcite lingo) and rules to efficiently query those sources. Calcite already has adapters that do things like read from HTML tables over HTTP, files on your file system, running processes, etc. This is in addition to connecting to a bunch of... - Source: Hacker News / almost 2 years ago
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