Real-Time Propagation, Not Batch Syncs
Subscribers receive new table versions automatically, ensuring dashboards, ML models, and applications operate on consistent data states.
Real-Time Data Integrations for Your Entire Ecosystem
Real-time data integrations become effortless with Tabsdata. Tabsdata connects directly to your warehouses, lakehouses, databases, operational applications, and streaming systems by publishing and propagating versioned tables through a declarative Pub/Sub for Tables model.
Instead of managing brittle sync jobs or scheduled workflows, Tabsdata keeps downstream systems consistent through automatic propagation of published table versions, with full lineage and reproducibility preserved end to end.
Explore All Tabsdata Integrations
Tabsdata supports a wide ecosystem of integrations across cloud platforms, warehouses, lakehouses, databases, SaaS applications, and streaming systems. Every integration participates in the same declarative, versioned dataflow model, ensuring deterministic propagation, built-in lineage, and reproducible dataflows across your stack.
Cloud & Infrastructure Integrations
Run Tabsdata securely within your private cloud environment while integrating seamlessly with major cloud providers.
Examples: AWS, GCP, Azure, OCI
Data Warehouses & Lakehouses
Deliver deterministic, real-time propagation into your analytical layer using complete, versioned table updates with built-in lineage and reproducibility..
Examples: Snowflake, BigQuery, Redshift, Databricks, Delta Lake
Database
Ingest structured data, CDC events, and operational updates from transactional databases and convert them into versioned tables ready for downstream consumers.
Examples: Postgres, MySQL, MariaDB, SQL Server, MongoDB
Streams & Messaging
Ingest event streams as structured, reproducible tables that automatically propagate through downstream dependencies.
Examples: Kafka, Pub/Sub, Kinesis
Analytics & BI Tools
Ensure dashboards always reflect a consistent data state by subscribing BI tools to versioned tables with preserved lineage.
Examples: Looker, Power BI, Tableau, Mode
Operational & SaaS Systems
Bring customer, marketing, logistics, and operational signals into a unified, governed dataflow without brittle ETL jobs or manual coordination.
Examples: CRM, ERP, marketing platforms, logistics systems
Custom Integrations
Build custom publishers and subscribers using Python, APIs, or SDKs to integrate any internal system into Tabsdata’s versioned dataflows.
How Tabsdata Integrations Work
Tabsdata integrations are not traditional connectors. Each source publishes structured data as tables, every publication produces a new immutable table version, and all dependent subscribers receive updates automatically based on declared relationships.
Propagation occurs as soon as table versions are published, without schedulers, polling, or orchestration. Lineage, reproducibility, and metadata are preserved across every source, transformation, and destination.
Why Teams Choose Tabsdata for Integrations
Tabsdata integrations operate within a deterministic execution model that keeps every system in your stack consistent and traceable.
Reproducibility + Time Travel Built In
All integrated datasets maintain immutable version history, allowing teams to audit, debug, or recreate past states without reprocessing.
Lineage Preserved Across All Integrations
Every source, transformation, and destination is tracked automatically, providing end-to-end visibility across the ecosystem.
Lower Maintenance Load
No DAGs, schedulers, or multi-tool workflows. Integrations update themselves through dependency-driven execution.
Enterprise Deployment Model
Tabsdata runs inside your VPC or private cloud, allowing integrations to operate securely within your existing infrastructure.
See How Tabsdata Integrates With Your Stack
Bring consistent, real-time data to every system your teams rely on.
