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, Botometer for Twitter should be more popular than Qrvey. It has been mentiond 8 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.
Since you're on AWS already, check out https://qrvey.com. Source: 6 months ago
A good starting point is to use large-scale network analysis on the bots to establish to what extent they are connected (e.g by mentions, retweets or follows) and then see if there are any outliers in the resulting network that look more organic (applying something like the Botometer API). Source: over 1 year ago
If in doubt about a user, ask them directly if they are being paid for their time online. Often the response is cagey, insulting, or redirecting, which should tell you everything you need to know. If they deny that they're being paid, check their post history to see how many hours a day they are posting, and which hours each day they are posting-- normal people do not have eight hours a day to post. Also check... Source: over 1 year ago
Https://www.pewresearch.org/internet/2018/04/09/bots-in-the-twittersphere/ "An estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings' Complete Report PDF (2018): https://www.pewresearch.org/internet/wp-content/uploads/sites/9/2018/04/PI_2018.04.09_Twitter-Bots_FINAL.pdf "Botometer": https://botometer.osome.iu.edu/ "Botometer (formerly BotOrNot) checks the... - Source: Hacker News / almost 2 years ago
You can start here. Musk running Twitter bots - for a long time at that - is pretty much common knowledge. Like this is how he became so insanely rich and why Tesla is so insanely overvalued by historical market KPIs. Source: about 2 years ago
MediaSmarts has developed a custom fact-checker search engine, which you can use to Google something you saw. All the results will be from verified fact-checkers. There’s also a tool that will tell you the likelihood that an account is a bot. Source: about 2 years ago
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