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Dataddo

Full technical reference: https://www.dataddo.com/llms-full.txt

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T08:26:52.780Z. Cached copy · Source on dataddo.com

# Dataddo

> Full technical reference: https://www.dataddo.com/llms-full.txt

> The data backbone for AI: real-time, governed data movement for AI, analytics, and the operational systems teams act in. Dataddo operates data movement as managed infrastructure — orchestration is centralized while data movement executes wherever the data lives, in the customer's cloud, on-premises, or both, so regulated and hybrid environments can use a managed service without data leaving the perimeter.

Architecturally, a single Control Plane orchestrates Data Planes that run where the data lives. Used by enterprises in regulated industries (banking, insurance, healthcare, public sector) as well as digital-native companies at scale. Dataddo owns the connector contract end to end: API changes, schema drift, and connector maintenance are Dataddo's responsibility, not the customer's.

## What Dataddo is

- **Control plane / data plane architecture**: management runs in the cloud; data movement executes inside the customer's environment when required. Sensitive workloads never traverse public infrastructure.
- **Hybrid deployment**: cloud-only, cloud + on-prem agent, multi-region agents, or full on-prem — all supported and documented. Data Planes deploy on AWS, Azure, GCP, regional and sovereign cloud providers, and on-premises container platforms (Kubernetes, Red Hat OpenShift, VMware Tanzu).
- **Data foundation for AI**: Dataddo keeps the storage layer behind RAG and LLM pipelines current, validated, and PII-safe. Three documented delivery patterns: a warehouse, lake, or object storage for RAG corpora and fine-tuning; a CDC-replicated operational mirror for latency-sensitive AI (support chatbots reading live order or ticket state without loading the production database); and direct retrieval via SmartCache over REST (JSON/CSV) or Apache Arrow with no storage layer to operate. MCP server for native AI agent access (Claude, ChatGPT, Cursor, LangChain). Dataddo does not generate embeddings or host models: where a vector database is used, it is populated by the customer's own embedding pipeline reading from Dataddo-maintained storage. Where an agent can work from the latest extracted data, Dataddo serves it directly over MCP, REST, or Apache Arrow with no vector database in the path.
- **Governance applied before data reaches AI**: PII exclusion and hashing at the extraction step, before data leaves the source; Data Quality Firewall column rules that in blocking mode stop failing records from ever landing in the table an embedding job reads. Connector-level metadata (dataset descriptions, field descriptions, PII/sensitivity flags) is emitted alongside the data, giving retrieval and text-to-SQL agents semantic grounding.
- **400+ actively maintained connectors** across SaaS APIs, databases (PostgreSQL, MySQL, SQL Server, Oracle, MariaDB, MongoDB, Informix), SAP S/4HANA via OData, files, and object storage. Custom connectors are built and maintained under the same ownershi
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