Velvet or Findly? A Comprehensive Review

Transforms team into data engineers

Enables data query from disparate sources

Offers real-time feature shipping

Allows complex SQL writing

Facilitates large data parsing

Turns queries into visual data

Data stored as query API endpoints

Supports real-time data for product development

Permits ingestion of any data source

Allows access to queries as API endpoints

Easy-to-use chatbot

Generates actionable data insights

No SQL or Python knowledge required

Uses natural language processing

Reduces Time To Insight (TTI)

Eliminates reliance on data professionals

Simplifies onboarding process

Saves software engineering time

Allows questions on Slack

Compatible with all SQL databases

Limited to high-growth startups

No explicit support for non-SQL languages

Real-time data might affect performance

Possible user overwhelm with unlimited data

Could favor tech-savvy users

No offline mode mentioned

Lack of data backup mechanisms

No explicit data security measures

Lacks APIs integration

Limited to SQL databases

Only works on Slack

No Python interpretation support

Possibly long chatbot response times

User-specific data interpretation issues

Lacks multi-platform support

No direct databank integration

Dependent on English proficiency

Limited onboarding efficiency improvements