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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.
Python QrveyNo features have been listed yet.
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, Python seems to be a lot more popular than Qrvey. While we know about 282 links to Python, 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.
Import aiohttp Import asyncio Async def fetch(session, url): async with session.get(url) as response: return await response.text() Async def main(): async with aiohttp.ClientSession() as session: html = await fetch(session, 'https://python.org') print(html) Asyncio.run(main()). - Source: dev.to / 17 days ago
Flat packages are the most common used packages, but distribution packages are more robust and can contain multiple flat packages. That's enough detail for this article but if you want to know more Armin Briegel of ScriptingOSX has a great book covering a lot of the details of these package types. I highly recommend picking up a copy for reference. One of the benefits of Distribution packages is that you can... - Source: dev.to / about 2 months ago
F-strings, introduced in Python 3.6 and later versions, provide a concise and readable way to embed expressions inside string literals. They are created by prefixing a string with the letter ‘f’ or ‘F’. Unlike traditional formatting methods like %-formatting or str.format(), F-strings offer a more straightforward and Pythonic syntax. - Source: dev.to / 4 months ago
Import aiohttp, asyncio Async def fetch_data(i, url): print('Starting', i, url) async with aiohttp.ClientSession() as session: async with session.get(url): print('Finished', i, url) Async def main(): urls = ["https://dev.to", "https://medium.com", "https://python.org"] async_tasks = [fetch_data(i+1, url) for i, url in enumerate(urls)] await... - Source: dev.to / 6 months ago
Threading involves the execution of multiple threads (smaller units of a process) concurrently, enabling better resource utilization and improved responsiveness. Python‘s threading module facilitates the creation, synchronization, and communication between threads, offering a robust foundation for building concurrent applications. - Source: dev.to / 6 months ago
Since you're on AWS already, check out https://qrvey.com. Source: 7 months ago
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