Datajoi provides an AI‑driven data management platform that automates data profiling, cleansing, and enrichment across heterogeneous sources, generating a searchable catalog and enforcing role‑based access controls. Built on DuckDB and DuckLake, it delivers sub‑second analytics on Parquet datasets and integrates via RESTful and SQL‑compatible APIs for BI tools and downstream pipelines. The solution reduces manual ETL effort, enabling data engineering and analytics teams to focus on analysis.
Funding
Funding not disclosed
Founders
Product
Problem
Many enterprises remain trapped in data silos with inconsistent data quality and limited context, making it difficult to derive reliable insights. Traditional ETL pipelines require extensive manual effort for data validation, cataloging, and governance, slowing time‑to‑value for analytics initiatives.
Solution
Datajoi delivers an AI‑driven data management platform that automates end‑to‑end data operations, turning static storage into autonomous business data workflows. Its Data Agents augment the Extract‑Transform‑Load process by continuously profiling, cleansing, and enriching datasets without human intervention. The platform automatically generates a searchable data catalog and enforces role‑based access controls, enabling users to discover and consume relevant data instantly. Built on DuckDB for ultra‑fast in‑memory analytics and DuckLake’s Parquet‑based lake architecture, Datajoi provides a lightweight, high‑performance foundation that integrates seamlessly with existing SQL environments. By offloading routine data‑ops tasks to AI, organizations can focus on strategic analysis rather than manual data preparation.
Target Audience
Primary customers are data engineering and analytics teams within mid‑size to large enterprises that need automated data quality, cataloging, and rapid query performance for business intelligence and data‑driven applications.
Features
- AI‑powered Data Agents that continuously monitor, validate, and enrich incoming data streams across heterogeneous sources
- Automated data quality management with anomaly detection, schema enforcement, and drift monitoring
- Dynamic data catalog generation using metadata extraction and semantic tagging for instant discoverability
- High‑performance query engine powered by DuckDB, delivering sub‑second analytics on large Parquet datasets
- DuckLake integration offering lakehouse capabilities without the complexity of traditional lakehouse stacks
- Fine‑grained role‑based access control (RBAC) and policy engine for secure data sharing across internal and external consumers
- RESTful and SQL‑compatible APIs for easy embedding of data services into BI tools, applications, and downstream pipelines
- Minimal infrastructure footprint with containerized deployment options for on‑prem, cloud, or hybrid environments