Data Ingestion is the entry point to the platform — batch, streaming, and change data capture unified into governed pipelines, with quality, lineage, and PII controls applied the moment data arrives.
Without governed data ingestion, every new source is a fresh liability — ungoverned, unattested, and disconnected from lineage the moment it lands.
Batch jobs, streaming feeds, and change capture are built in isolation with separate tools — so governance is inconsistent and gaps appear at every boundary.
When quality is validated after ingestion, schema drift, nulls, and stale records flow straight into pipelines and reach AI agents before anyone notices.
Sensitive fields arrive in raw form and sit unmasked until a downstream process catches them — if it ever does. Governance must start at ingest, not after.
Choose from 100+ connectors — databases, SaaS, streaming, files, and APIs.
Batch, streaming, or change data capture — the right pattern per source, unified.
PII auto-detected and masked, quality gates applied, lineage captured at the first byte.
Governed, attested data lands ready for federation, analytics, and AI agents.
Batch ETL, real-time streaming, and change data capture under one governed framework — no separate tools, no governance gaps between them.
Governance-authored quality rules block schema drift, nulls, and stale records before they enter pipelines — failures caught at the door, not in a report.
Sensitive fields are detected and masked the moment data is ingested, so personal data is never exposed in raw landing zones.
Every ingested record carries lineage from its source, so the audit trail begins at the first byte and stays unbroken to the final decision.
Stream high-velocity events and batch-load historical data through the same governed pipeline, so AI agents always work on current, complete data.
Relational databases, cloud warehouses, data lakes, SaaS apps, message queues, files, and APIs — all governed identically on ingestion.
Data ingestion is the process of bringing data from source systems into a platform for storage, processing, and analysis. Tantor's data ingestion unifies batch, streaming, and change data capture into governed pipelines, applying quality, lineage, and PII controls from the first byte.
Tantor data ingestion supports batch ETL for historical loads, real-time streaming for high-velocity events, and change data capture (CDC) for row-level updates — all under one governed framework, so each source uses the right pattern without separate tools.
Governance is applied at ingest, not after. Tantor automatically detects and masks PII, enforces quality gates, and captures lineage the moment data arrives — so data is attested and audit-ready before it reaches any pipeline or AI agent.
Yes. Tantor data ingestion handles real-time streaming and batch loads through the same governed pipeline, so historical and live data are unified — ensuring AI agents and analytics always work on current, complete, governed data.
See how Tantor Data Ingestion unifies batch, streaming, and change data capture into governed pipelines — quality, lineage, and PII controls built in.