Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that. CData Arc Enterprise Info View purchase options Pricing is based on actual usage, with cha...
Pricing Features Loading... Ricardo S. Gambling & Casinos Enterprise(> 1000 emp.) Validated Reviewer Review source: G2 invite Incentivized Review Jun 13, 2023 "Fast data" What do you like best about CData Connect Cloud? I like the most have the data constantly updated on time!Review collect...
Features Connectors Use Cases Documentation Resources Pricing Sign In Available data connectorsWith 200+ ready-to-use connectors, you can easily manage your data across your data sources, data storages and analysis tools. All Data Sources Data Warehousing Analytics & Reporting Tools 3PL ...
CData Virtuality is an independent semantic layer that bridges the gap in your heterogeneous data landscape through four key pillars: connectivity, modeling, governance, and data product delivery.
Data Warehouse, Databases Data Lake, No SQL Storages Data Lakehouse Online Analytics Streaming, Kafka Cloud, API, SaaS Files (CSV, JSON, XML) 300+ Ready to see more? Start the Product Tour Book a Demo CData Virtuality recognized in Gartner®Magic Quadrant™ for Data Integration Tools ...
Because I am 3rd party with several customers the pricing is not reliable and the jump can be high. There might be more features, but not always needed Reason for choosing CData Arc At the time, cost and the simplicity of the software Alec Verified reviewer Transportation/Trucking/Railroad, ...
Single Server License One-year Subscription $2,495 On Premise per user Entry-level set up fee? No setup fee For the latest information on pricing, visithttps://www.cdata.com/dbamp/#purchase Offerings Free Trial Free/Freemium Version Premium Consulting/Integration ServicesReturn to navigation Produc...
CData Virtuality is an independent semantic layer that bridges the gap in your heterogeneous data landscape through four key pillars: connectivity, modeling, governance, and data product delivery.