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Frequently Asked Questions
How does Knowi handle NoSQL data without ETL?
It uses native data connectors to query NoSQL databases like MongoDB and Elasticsearch directly. The platform can process nested JSON and complex schemas in their original format, eliminating the need to flatten the data or move it into a separate warehouse for analysis.
What is the difference between NLQ in Knowi and Tellius?
The NLQ in Tellius is highly optimized for querying structured data within a pre-defined semantic layer or cloud data warehouse. The NLQ in the competing platform is designed to work directly on unmodeled, raw data from both SQL and NoSQL sources, enabling more ad-hoc and exploratory analysis.
Can Tellius connect directly to MongoDB?
Tellius can connect to MongoDB, but it typically requires the data to be prepared and modeled, often by flattening the nested JSON structure into a tabular format that its analytics engine can process more effectively. It is not designed for native analysis of complex, nested documents directly from the source.
What are the Private AI options for secure analytics?
Private AI involves deploying the entire analytics platform, including the LLMs, within a customer’s own private environment, such as an on-premise data center or a virtual private cloud (VPC). This ensures that no sensitive data is ever transmitted to a third-party service, meeting strict data residency and security requirements for industries like healthcare and finance.
How do these platforms handle embedded analytics for SaaS?
Both platforms provide white-label embedded analytics with robust APIs. Key differentiators include support for multi-tenant security architectures to ensure data isolation between customers and the ability to embed analytics built on live, multi-source data without requiring an underlying data warehouse for each tenant.
Which tool is better for root cause analysis on cloud warehouses?
Tellius is purpose-built for automated root cause and key driver analysis on structured data within cloud warehouses like Snowflake, BigQuery, and Databricks. Its AI, Kaiya, excels at automatically surfacing the "why" behind changes in business metrics, making it a stronger choice for this specific use case.
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