GRASS GIS VS Qrvey
Compare GRASS GIS VS Qrvey and see what are their differences
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GRASS GIS
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Qrvey
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GRASS GIS offers powerful raster, vector, and geospatial processing engines in a single integrated software suite. It includes tools for terrain and ecosystem modeling, hydrology, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It comes with a temporal framework for advanced time series processing and a Python API for rapid geospatial programming. GRASS GIS has been optimized for performance and large geospatial data analysis.
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.
GRASS GIS QrveyNo features have been listed yet.
GRASS GIS's answer
GRASS GIS primarily caters to geospatial professionals, researchers, and students in fields like geography, environmental science, urban planning, and geology. It is also used by government agencies and non-profit organizations for spatial data analysis and environmental modeling.
Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
GRASS GIS's answer
As an open-source tool, GRASS GIS doesn't have "customers" in the traditional sense. However, it is widely used by various government agencies, academic institutions, and environmental organizations worldwide. Notable users include space agencies, numerous universities and research institutions as well as companies involved in geospatial studies and analysis.
GRASS GIS's answer
GRASS GIS was initially developed by the U.S. Army Corps of Engineers as a tool for land management and environmental planning. It was first released in the early 1980s and has since evolved into a robust, multi-functional GIS platform, largely due to contributions from a global community of developers. GRASS GIS is a founding member project of the Open Source Geospatial Foundation (OSGeo.org).
GRASS GIS's answer
Qrvey's answer:
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.
GRASS GIS's answer
Qrvey's answer:
Customers choose Qrvey for the following reasons:
GRASS GIS's answer
GRASS GIS is primarily written in C, Python, and C++. It uses a range of geospatial libraries and technologies, including GDAL for data conversion, PROJ for coordinate transformations, and can interface with SQL databases.
Based on our record, GRASS GIS 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.
Https://grass.osgeo.org/- Source: Hacker News / 2 months agoGRASS GIS offers powerful raster, vector, and geospatial processing engines in a single integrated software suite. It includes tools for terrain and ecosystem modeling, hydrology, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It comes with a temporal framework for advanced time series...
We haven't looked at integrating GRASS yet, as we're more interested in data display, not deep analysis. Just another example of a C/C++ library with front end bindings for Python. Numbers are crunched in C/C++, results returned to Python. Source: 11 months ago
Anyone have good advice for where to learn how to use GRASS. Source: 11 months ago
Outside of personal experience, based on second-hand insight: GRASS is an extremely powerful tool, if you're not familiar with it already, and you can use it from the CLI and from Python. If you'd like to step out of Python at some point, I hear Java is used a lot for enterprise GIS, while Julia looks like the language of the future (especially now with JuliaGeo), but that still remains to be seen. Source: over 1 year ago
Sometimes some modules from GRASS like r.lake at the moment. Source: over 1 year ago
Since you're on AWS already, check out https://qrvey.com. Source: 5 months ago
QGIS - QGIS is a desktop geographic information system, or GIS.
DevicePilot - DevicePilot is a universal cloud-based software service allowing you to easily locate, monitor and manage your connected devices at scale.
ArcGIS - ArcGIS software is a data analysis, cloud-based mapping platform that allows users to customize maps and see real-time data ranging from logistics support to overall mapping analysis.
AnswerRocket - AnswerRocket is a search-powered analytics that makes it possible to get answers from business data by asking natural language questions.
SAGA GIS - SAGA - System for Automated Geoscientific Analyses - is a Geographic Information System (GIS)...
Syndigo - Syndigo is an online management platform that provides access to the world’s biggest global content database of digital information.