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Verified User in Information Technology and Services
Provides a familiar spreadsheet environment improving time to value for developers and adoption for consumers.
Does not offer a traditional spreadsheet interface; relies on search and AI-driven analytics for data exploration.
Use familiar spreadsheet skills for data analysis as well as direct SQL queries.
Users need to adapt to a search-based interface, leveraging natural language queries and AI capabilities.
Right-click on any element to drill down into further analysis without additional set-up.
Supports interactive drill-downs and infinite drill-downs for in-depth data exploration.
Enables dynamic data summarization and complex analysis. Easy multi-level groupings and customization.
Lacks traditional pivot table functionality but offers advanced data exploration and visualization through AI-driven insights.
Easy to combine user input context, UI actions, and data warehouse to build data applications. Easy to input cell level data or upload CSVs to the warehouse.
Does not support write-back functionality.
Connects seamlessly to real-time data sources.
Connects to various cloud data platforms for live-querying and provides real-time insights.
Leverages intelligent query engine for performant queries.
Optimized for live-querying cloud data; uses AI to enhance query performance and provide quick insights.
Utilizes warehouse caching mechanisms to securely enhance performance and reduce query times.
Relies on live-querying cloud data, with less emphasis on data caching.
Supports real-time collaboration while building data projects.
Does not support real-time collaborative editing but allows for shared notebooks and collaborative insights.
Provides a robust SQL editor allowing for analysts to do ad-hoc analysis and share results.
Does not focus on SQL editing; users interact with data through natural language queries and AI.
Integrates Python for scripting and advanced data analysis.
Does not support Python scripting natively.
Tracks changes to everything, allowing granular reversion.
Does not offer native version control; focuses on governed data models for consistency.
Shows a detailed view of data origin and transformations; each element is a data source.
Provides some data lineage through its governed data models but lacks detailed lineage tracking for all data elements.
Offers assistance and resources via live chat for all users within the platform.
Offers community support and documentation, but immediate in-product support is not emphasized.
No-code UI customization and simple iFrame embedding requires minimal developer resources and no custom SDK integrations.
Requires extensive upfront worksheet building and model training to effectively utilize natural language search, leading to longer set up and implementation times.
Flexible platform with the ability to seamlessly switch between spreadsheets, SQL, or Python, means fewer development and maintenance resources required, more frequent updates, and accelerated product velocity.
Search-driven interface can handle simple queries but becomes cumbersome when users try to answer secondary queries due to the ongoing need for data model management and maintenance.
Intuitive, spreadsheet interface simplifies data exploration and dashboarding, empowering users to drill down into data without technical expertise.
Search is not natural language-based and requires user training on query framing, leading to limited exploration and low user adoption as new requirements must be modeled before accessing data.
Built to scale with predictability of cost as actual usage grows and custom license types for flexible paths to monetization.
Complex tiering and expensive consumption based pricing make estimating warehouse costs near impossible.
Real-time dedicated in-app support for your entire organization with under a minute response times and the option to schedule a live call with a support team member.
In-app chat support lacks human touch with predominantly canned responses and only offered at higher pricing tiers.
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