Every pattern is its own stack
Different vendors, different configs, different failure modes - and the knowledge of how each one works lives with whoever set it up.
Solutions / Single Ingestion Layer
The single ingestion layer for your enterprise.
Batch, real-time CDC, streaming, and direct delivery - run from one place, executed wherever your data lives, cloud or on-prem. One place to connect, govern, and monitor every pipeline in the organization, and to feed clean, current data to your analytics and AI agents alike - instead of a different tool for every pattern.
One backbone for every pipeline, every pattern, every AI use case.
The problem
Data integration grows one tool at a time - until nobody governs the whole.
It starts innocently: one tool for batch loads, another for CDC, a script for streaming, something ad hoc for the AI team. A year later, nobody owns the whole picture.
Sensitive data creates exceptions
The moment data can't leave your premises or region, cloud-only tools force workarounds - and your most sensitive pipelines end up the least governed.
Security reviews multiply
Each tool has its own access model, its own logs, its own gaps. Confirming who moved what data where means asking every system separately.
Consolidating ingestion onto one platform isn't tidiness - it's how governance, security, and cost stay controllable as data use grows.
How it works
Every pattern, one platform.
Different destinations and use cases need data delivered differently. Dataddo runs every one of these patterns, so the shape of the movement is a configuration choice, not a platform decision:
| Pattern | What it does | Typical consumer |
|---|---|---|
| Batch (ETL/ELT) | Scheduled, analytics-ready loads, on schedules down to one minute where the source allows it | Warehouses, lakes, reporting |
| Real-time CDC | Every committed insert, update, and delete, streamed from the database transaction log | Operational analytics, AI on live data |
| Streaming | Publishes data as messages to your event backbone, with a no-caching guarantee for compliance-sensitive pipelines | Kafka, Azure Event Hubs, stream processors |
| Direct delivery | Data served straight from Dataddo's SmartCache - no storage layer for you to operate | Dashboards, AI agents via MCP, REST, or Apache Arrow |
The patterns compose: the same source can feed the warehouse in batch, the event backbone as a stream, and an AI agent directly - all under one governance layer.
Architecture
One control plane. Data planes wherever your data must stay.
You manage every pipeline from a single cloud control plane - one interface, one API, one place to see everything. The data itself moves through data planes deployed where you need them: public cloud, your own cloud, or on-prem, next to the systems holding your most sensitive data. Records that must never leave the building, don't - and they still show up in the same monitoring, governance, and audit trail as everything else.
| Layer | Location / role |
|---|---|
| Control plane (cloud) | UI, API, governance, monitoring, audit |
| Data plane: public cloud | AWS, Azure, GCP |
| Data plane: your cloud | Your VPC, your region |
| Data plane: on-prem | Next to sensitive systems |
Every destination
Wherever the data needs to land.
BigQuerySnowflakeDatabricksRedshiftSynapseAmazon S3Azure BlobKafkaEvent HubsDashboardsBusiness appsAI models & agents
+ many more
Every user
Four kinds of users. One governed platform.
| User | Interface | Experience |
|---|---|---|
| Business teams | UI | Build and monitor pipelines without an engineering ticket. |
| Engineers | API | The full platform, headless - a REST API for everything the UI does. |
| Automations | Programmatic | Pipelines triggered, monitored, and woven into existing workflows. |
| AI agents | MCP | Agents retrieve fresh data and create, inspect, and monitor pipelines conversationally. |
Everyone works against the same pipelines, the same permissions, the same audit trail - so self-service never turns into shadow IT.
Enterprise fabric
Built to plug into how your enterprise already runs.
Your identity provider is the front door.
Single sign-on and enterprise IAM integration - your team signs in with the credentials IT already manages, and IT keeps central control over who can access what.
An audit trail your auditors accept.
Account-level logs capture every login and permission change; action-level logs record every extraction, transformation, and write. Export them to your SIEM and security review becomes reading, not archaeology.
Alerts where your team already looks.
24/7 monitoring of every source, flow, and destination, with notifications in-app, by email, or by webhook - into Slack, your monitoring platform, or any system that takes an HTTP call.
Orchestration-friendly by design.
Webhook events on every extraction and flow run let your orchestrator react to Dataddo, and the API lets it drive Dataddo in return. Works alongside Airflow, dbt, and whatever already runs your data operations.
Compliance is table stakes here.
SOC 2, ISO 27001, GDPR, HIPAA, CCPA, LGPD, and POPIA - independently verified. TLS in transit, AES-256 at rest with HSM-backed key management, and credentials encrypted and network-isolated.
Private connectivity included.
SSH tunneling for secure transfer to on-prem and cloud storage, and network ACLs so you control exactly which connections are permitted.
Time to value
Consolidate gradually. Govern immediately.
Nobody migrates all ingestion in one quarter, and you don't have to. Start with the pipelines that hurt most - every pipeline you move lands under the same control plane, the same audit trail, and the same alerting from day one. The platform grows with each migration; the governance is whole from the first one.
Talk architecture with an engineer.
Bring your patterns, your destinations, and your compliance constraints - leave with a consolidation path onto one ingestion platform.