Tabsdata is a real-time ETL system built on a table-centric pub/sub model. Every time upstream data is published, a new table version is created and propagated instantly through a dependency graph. No pipelines to orchestrate. No DAGs to build and maintain. No streaming frameworks to manage. You get real-time, deterministic, reproducible data across all downstream systems.
Engineers know the problems firsthand:
Tabsdata introduces a simple mechanism: When a table updates, a new version is published. All subscribers receive and process that version immediately.
Deterministic propagation
Automatic dependency resolution
Consistent outputs across all environments
Real time behavior without streaming infrastructure
Adding new data sources is as simple as registering a new publisher. Adding new data consumers is as simple as registering a new subscriber. And every update comes with a fully reproducible lineage including pointers to the specific table versions that participated in that data flow. There are no pipelines to build or maintain. Adding a subscriber automatically includes it in the dataflow from upstream.
No end-to-end batch pipeline creation or orchestration. You publish and the system handles the rest.
An immutable table version
Enriched with metadata, schema and lineage
Immediately available for downstream use.
No end-to-end batch pipeline creation or orchestration. You publish and the system handles the rest.
Transforms are written as Python functions using TableFrame API. Each function runs in an isolated sandbox with its own dependencies. Tabsdata guarantees:
Deterministic execution of transformation functions
Recording of input dataset version and output dataset version
Automatic ordering of dependent transformations to ensure deterministic operation
Engineers focus on logic. Tabsdata handles execution in a transactionally consistent manner across the entire dependency graph.
When a table updates, Tabsdata:
Creates a new version for that table
Resolves dependencies dynamically
Runs the required transformations
Propagates updates to all dependent tables and subscribers
Delivers fresh data to every downstream system
There are no DAGs to author or schedule, removing the need for complex imperative logic with ad-hoc manipulation. All transformations are recorded within the dataflow lineage along with the state of all inputs and outputs, making it a breeze to debug and trace hard to find logical data problems when needed.
Every table version is tracked with complete lineage. You can:
Inspect exactly which upstream versions produced a given output
Inspect the version of the transformation function that executed to produce the output
Recreate any state of the system
Roll back instantly if needed and reprocess anything from within a data flow graph
Debugging becomes a direct inspection exercise, not a pipeline archeology project.
Tabsdata runs inside your on-premises or private cloud environment.
You keep full control of infrastructure and governance.
Engineers get something simple: systems that stay correct and are easy to inspect and validate. Tabsdata removes:
Tabsdata supports real-time ETL workloads across:
Low latency replication into warehouses and lakehouses. Moreover, data engineers can produce CDC outputs for any data source using set comparison between the latest and previous version of the published table.
Fresh KPIs with no batch windows to wait for. Moreover, all data consumers see a consistent view of all data sources due to data sources being virtualized by published materialized tables.
Synchronized feature tables for training and inference.
Instant propagation of signals.
Real time combination of operations and contextual data.
Deterministic, reproducible data preparation at scale that keeps the complexity of development and operations in check perpetually.
All powered by the same underlying mechanism: publish, transform, and propagate table versions.
Unmanaged dependencies that have to be managed manually through execution ordering or other mechanisms.
External orchestration using imperative systems like DAGs or notebooks that are hard to build, harder to extend and difficult to maintain.
Manual reconciliation of data drift, with little to no support for semantic drift.
Fragile restarts and manual reprocessing on encountering problems. Layering observability stack on top of these pipelines can lead to more false positives than identifying real issues.
Multiple tools and frameworks glued together to get data into consumption ready state, with hand-offs between layers losing context, diluting metadata and dropping semantics.
No orchestration needed. Changes are propagated by Tabsdata to all dependent recipients automatically when new data is published anywhere.
Deterministic propagation: with dynamically computed dependencies, you can rest assured that all data consumers will see the correct and consistent up-to-date data together.
Immutable versions with built-in lineage ensure that all data flows are fully inspectable, verifiable and reproducible. This eliminates the need for complex debugging through log-scraping and analyzing query execution plans.
Built-in data observability through direct inspection of table versions, data quality reports and ad-hoc inspection via SQL access.
Tabsdata gives you a new foundation for moving data in real time. One that cuts operational load, reduces data requests, and gives engineers control through clarity rather than complexity.
