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No row limits enables users to analyze massive datasets without restrictions
Ad-hoc calculations are limited by arbitrary results, starting as low as 500 rows; self-service calculations are based on limited data, leading to results that are inaccurate and misleading
Flexible paths allow users to drill into any data point using any field for further analysis without additional set-up
Drill paths in OmniML are rigid and predefined, limiting users’ ability to explore metrics on their own
Explore data freely using familiar spreadsheet syntax and a no-code GUI, without relying on data teams or predefined questions
Accurate answers require OmniML, limiting iterative exploration and self-service by forcing reliance on predefined questions or the data team
Multiple editors can collaborate in real-time with data securely written to the warehouse; version control and RLS/CLS ensures governance and prevents data loss from overwrites
Only supports CSV uploads, limiting collaboration and lacking governance; lacks robust front-end and writeback needed for line-of-business data applications
Build applications directly on your data warehouse; collect and writeback data to the cloud, automate workflows, and trigger actions
Does not support native data application development; CSV uploads have a fixed record limit and by default, do not write data back to the database
Flexible layout and design allow for granular control and easy to understand interfaces for end users
Rigid layouts force filters to the top of dashboards; customization limited to individual visualizations
Flexible data modeling with modular governance that can be leveraged across the entire organization—no proprietary languages required
Modeling layer enforces business logic and metric definitions in code (OmniML) making them inaccessible to non-technical users due to reliance on code and proprietary modeling languages
Utilizes warehouse caching mechanisms to securely enhance performance, reduce query times, and ensure data is never stored or exposed in external caches
Utilizes external caching mechanisms to improve performance but poses a security risk by storing proprietary and sensitive data outside the warehouse
Custom functions insure teams can leverage everything done in the warehouse; AI query, UDFs, ML models, and native functions like Cortex in Snowflake
Users must know SQL to both create and leverage with these functions limiting their reach and capabilities
Tracks changes to dashboards, enabling granular reversion and management of visual updates, enabling the entire organization to make their own changes with the safety of full rollbacks
Version control is managed externally in tools like GitHub, GitLab, or Azure DevOps, requiring third-party skillsets
Provides a robust SQL editor that can effectively handle mixed querying allowing for analysts to do ad-hoc analysis and share results
Supports custom SQL queries but does not allow formixed querying, limiting analysis
Offers assistance, resources, and robust documentation via live chat for all users within the platform
Offers documentation and community FAQs but no immediate in-product support
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