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+ Only one here with Two-factor authentication, GDPR compliance, Multi-language

+ Vendor is publicly listed — audited financial disclosure

+ Broadest feature coverage (12)

+ Only one here with Workflow automation

+ Can run on-premise

+ Most deployment options (Cloud (SaaS), On-premise, Hybrid)

+ Free plan available

+ Vendor is publicly listed — audited financial disclosure

+ Largest vendor (183,000 employees)

+ Longest track record — vendor founded 1998

− No free plan

− Cloud only

− No public adoption figures

− Smallest vendor (4,000 employees) — check support capacity

− No free plan

− Cloud only

− Lacks SOC 2, ISO 27001, HIPAA — offered by others here

− No free plan

− No public adoption figures

− Narrowest feature scope (7)

− Lacks Dashboards — offered by others here

− Cloud only

− No public adoption figures

+ Unified lakehouse for data engineering and AI

+ Built on open formats like Delta Lake and Spark

+ Strong collaborative notebooks and ML tooling

+ Separate storage and compute scale independently

+ Near-zero maintenance as a fully managed service

+ Strong data sharing and multi-cloud support

+ Visual and code-based workflows in one platform

+ Enables collaboration between analysts and data scientists

+ Flexible deployment across clouds and on-premise

+ Serverless with no infrastructure to manage

+ Fast SQL analytics on very large datasets

+ Built-in ML and tight Google Cloud integration

− Usage-based costs can be hard to predict

− Requires data engineering expertise

− Cluster tuning and governance need attention

− Usage-based costs can be hard to predict and control

− Requires governance to avoid runaway warehouse spend

− No on-premise deployment option

− Licensing is expensive for smaller teams

− Platform breadth brings a learning curve

− Performance depends on underlying infrastructure

− Query costs can be hard to predict

− Best fit for Google Cloud-centric stacks

− Cost optimization requires query discipline