Best Desktop (2025)
Ranked picks for desktop. No "it depends."
🧊Nice Pick
Looker
The BI platform that makes data modeling feel like a full-time job, but at least the dashboards look pretty.
Full Rankings
#1
Details →Looker
Nice PickThe BI platform that makes data modeling feel like a full-time job, but at least the dashboards look pretty.
Pros
- +Powerful LookML modeling language for reusable, version-controlled data definitions
- +Seamless integration with modern data warehouses like BigQuery and Snowflake
- +Self-service analytics that empowers non-technical users to build custom reports
Cons
- -Steep learning curve for LookML, requiring dedicated data engineers or analysts
- -Pricing can be prohibitive for small teams, with enterprise-focused costs
Excel's smarter cousin that makes data pretty, but good luck escaping Microsoft's ecosystem.
Pros
- +Seamless integration with Microsoft 365 and Azure
- +Intuitive drag-and-drop interface for quick visualizations
- +Powerful DAX language for complex calculations
- +Affordable pricing with a robust free tier
Cons
- -Performance can lag with large datasets
- -Limited customization compared to open-source alternatives
Compare:vs Looker
The Picasso of dashboards—beautiful, powerful, and priced like a masterpiece.
Pros
- +Drag-and-drop interface makes it accessible for non-technical users
- +Stunning, interactive visualizations that impress stakeholders
- +Robust data connectivity with support for various sources like Excel, SQL, and cloud services
- +Advanced analytics features for data professionals, including predictive modeling
Cons
- -Expensive licensing can be a barrier for small teams or startups
- -Steep learning curve for mastering complex features and customizations
The crystal ball of data science. Turns historical patterns into future guesses, with a side of overconfidence.
Pros
- +Enables data-driven forecasting for decisions like sales or churn
- +Leverages machine learning to uncover hidden patterns in historical data
- +Scalable across industries from finance to healthcare
Cons
- -Heavily reliant on quality data; garbage in, garbage out
- -Models can overfit and fail in real-world scenarios
Head-to-head comparisons
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