Data Virtuality vs Invantive SQL

Struggling to choose between Data Virtuality and Invantive SQL? Both products offer unique advantages, making it a tough decision.

Data Virtuality is a Business & Commerce solution with tags like etl, data-virtualization, data-management.

It boasts features such as Data integration and ETL, Data virtualization and federation, Connects to diverse data sources, Unified access to distributed data, Data modeling and mapping, Data quality functions, Metadata management, Monitoring and management console and pros including Integrates data from many sources, Provides real-time data access, Improves data quality, Reduces data duplication, Easy to use graphical interface, Scalable architecture.

On the other hand, Invantive SQL is a Office & Productivity product tagged with sql, data-analytics, reporting, excel-addin.

Its standout features include Allows SQL access to data sources like SAP, Salesforce, Dynamics 365, etc. directly from Excel, Enables creating reports, dashboards, and visualizations in Excel using live data, Provides over 370 functions for data transformation and analysis, Includes a formula engine for complex calculations, Allows scheduling and distributing reports via email or file shares, Integrates with Power BI and Power Query, and it shines with pros like No data replication or caching needed, Easy for Excel users to create reports without IT help, Real-time data access improves accuracy of reporting, Reduces reliance on IT for analytics needs, Works across platforms like Windows, Mac, Linux, and the web.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Data Virtuality

Data Virtuality

Data Virtuality is a data integration and data management platform that connects various data sources and makes data available for analytics and applications. It offers ETL capabilities as well as data virtualization to provide unified access to distributed data.

Categories:
etl data-virtualization data-management

Data Virtuality Features

  1. Data integration and ETL
  2. Data virtualization and federation
  3. Connects to diverse data sources
  4. Unified access to distributed data
  5. Data modeling and mapping
  6. Data quality functions
  7. Metadata management
  8. Monitoring and management console

Pricing

  • Subscription-Based
  • Pay-As-You-Go

Pros

Integrates data from many sources

Provides real-time data access

Improves data quality

Reduces data duplication

Easy to use graphical interface

Scalable architecture

Cons

Can have a steep learning curve

Limited community support

Not ideal for large-scale big data needs

Lacks some advanced data science capabilities


Invantive SQL

Invantive SQL

Invantive SQL is an Excel add-in for data reporting and analytics that provides SQL access to 100+ data sources without copying data. It enables business users to create real-time reports, dashboards and visualizations in Excel connected to data sources.

Categories:
sql data-analytics reporting excel-addin

Invantive SQL Features

  1. Allows SQL access to data sources like SAP, Salesforce, Dynamics 365, etc. directly from Excel
  2. Enables creating reports, dashboards, and visualizations in Excel using live data
  3. Provides over 370 functions for data transformation and analysis
  4. Includes a formula engine for complex calculations
  5. Allows scheduling and distributing reports via email or file shares
  6. Integrates with Power BI and Power Query

Pricing

  • Freemium
  • Subscription-Based

Pros

No data replication or caching needed

Easy for Excel users to create reports without IT help

Real-time data access improves accuracy of reporting

Reduces reliance on IT for analytics needs

Works across platforms like Windows, Mac, Linux, and the web

Cons

Requires learning SQL for full capabilities

Limited options for data visualization compared to dedicated BI tools

Not optimized for very large datasets

No built-in data preparation or ETL capabilities