Dataddo

Dataddo is a no-code data integration and ETL platform for analytics and data teams, sold per seat or by data flow, with optional self-hosted Data Planes.

Dataddo moves data out of the apps, databases and files where it is created and into the warehouses, dashboards and AI tools where people use it. Dataddo Inc. built the platform after its CEO, Petr Nemeth, kept meeting data stuck in the tools that generated it. The company designed it as one no-code tool for every kind of integration, with an API for engineers who want lower-level control. Data Anywhere is the integration product: it connects any source to any destination by ETL, ELT, change data capture, reverse ETL or streaming. Database replication runs on the Mesh Ingestion engine, which reads tables in parallel and then keeps them current with incremental or log-based CDC. Data to AI serves governed business data to Claude, ChatGPT and other assistants over MCP, with read-only access and personal fields hashed first. The Data Quality Firewall checks every load for nulls, zeros and per-column anomalies before it lands, while schema drift is detected and handled by configurable rules. A cloud Control Plane runs every pipeline, and Data Planes can sit in a private cloud or on premises for sensitive data. Analysts and business teams in marketing, sales and operations are the first audience, since they can build a pipeline to a dashboard without waiting on engineering. Data engineers get a documented REST API that controls every part of the platform, so pipelines can be scripted rather than clicked. Companies that must keep sensitive data in their own environment run Data Planes there and manage them from the same console. Teams connecting AI assistants to company data work through a governed layer of field and metric definitions instead of raw access to each source. Organisations serving several teams or clients can separate them into tenants. A common job is landing sales, marketing and finance data in Snowflake, BigQuery or Databricks on a schedule from minutes to daily, then reporting on it in a BI tool. Database teams replicate production systems with log-based CDC, or publish each committed change as messages to Kafka topics and Event Hubs. SAP S/4HANA data can be extracted into the same cloud stack, and teams building a lake write Parquet or Apache Iceberg tables to storage they own, Microsoft OneLake included. When a sync fails, a notification arrives in the app, by email or through a webhook into Slack. Dataddo is hired to keep each of those copies current without a team maintaining its own connectors.