Real-Time Data Integration Solutions for Modern, AI-Driven Systems

Real-time ETL delivers data to downstream systems automatically as soon as it is published, without waiting for scheduled batch runs.

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Batch ETL runs on a fixed schedule, often hourly or daily, pulling large volumes of data through pipelines that operate in discrete chunks. Real-time ETL processes data as soon as it becomes available, reducing the delay between when data is produced and when it can be used. Batch ETL is simpler but slower and prone to data inconsistencies and staleness, while real-time ETL keeps downstream systems fresher without waiting for scheduled jobs or large batch processing windows.

Enterprises need real-time ETL because many decisions now depend on up-to-date operational data. Batch pipelines introduce inconsistencies and delays, causing dashboards to lag, ML features to drift, and business workflows to react too slowly. Real-time ETL reduces this latency by keeping downstream systems continuously current, which supports faster detection of issues, better customer experiences, and more reliable data for analytics, machine learning, and AI.

Traditional ETL depends on scheduled batch jobs, which makes data slow to update and hard to keep consistent as systems change. Pipelines become brittle over time, breaking when schemas shift or new sources are added, and they require constant maintenance to stay reliable. Because each job runs in isolation, it’s difficult to trace lineage, manage dependencies, or guarantee that the downstream consumers see a consistent, up-to-date view of the data.

Streaming pipelines are powerful but difficult to build and operate at scale. They require specialized skills, constant tuning, and careful management of state, ordering and fault recovery. Small changes in schemas or data volumes can ripple into outages, and debugging issues across distributed components is slow and unpredictable. As they grow, these systems become costly to maintain, and many teams struggle to keep them accurate, reliable, and aligned with downstream consumers.

Tabsdata uses tables as the unit of data propagation. When an upstream table is updated, the platform automatically identifies which downstream tables depend on it and generates new versions for them. Transformations run on complete, consistent inputs, and Tabdata manages dependency tracking, ordering, and propagation without pipelines or orchestration. This keeps dataflows simple, reliable and fully reproducible.

Most real-time ETL tools rely on pipelines, jobs, and streaming engines to move data, which makes them complex to build and expensive to operate. Tabsdata takes a different approach: it uses tables as the unit of propagation, automatically updating downstream tables whenever upstream tables change. There are no DAGs, no orchestration layers, and no streaming infrastructure to maintain. Because all tables are versioned and consistent, Tabsdata delivers real-time data with built-in lineage, reproducibility, and far lower operational overhead than traditional real-time systems.

Tabsdata connects seamlessly to transactional databases, SaaS apps, APIs, log and IoT feeds, and delivers them to systems that require them. Tabsdata connects seamlessly to filesystems, object stores, databases, SaaS applications and specialized systems like message brokers and IoT systems. For a full list of systems that Tabsdata connects with, refer to the Publisher and Subscribers section of documentation available at docs.tabsdata.com.

Tabsdata evaluates every update through a system-generated execution plan. As each plan runs, Tabsdata records the exact table versions and transformation code used, creating a complete execution artifact. When the dataflow finishes successfully, that artifact becomes the table’s lineage, giving teams a precise and reproducible history of how each version was produced. Tabsdata also supports table-level quality checks that run automatically with every new version, with results stored alongside the table’s metadata for easy inspection and monitoring.

Most teams can get started with Tabsdata in a matter of days, since the platform doesn’t require pipelines, orchestration setups, or bespoke streaming infrastructure. A small proof of concept usually involves connecting one or two sources, defining a few transformations, and publishing the outputs into your existing systems. Full implementation depends on the number of dataflows you want to migrate. Many teams roll out Tabsdata incrementally over a few weeks or months, expanding as they see value and gain confidence in the model.

Tabsdata is priced per core of execution, grouped in 8 core increments. For more information, see the pricing page.

Tabsdata can work alongside your current ETL and streaming systems or gradually replace parts that no longer meet your performance or maintenance needs. Most teams begin with workloads that need fresher data or are difficult to keep stable in pipelines or streaming engines. As they get comfortable with the model, many choose to simplify their stack by moving more dataflows into Tabsdata. Tabsdata works best for teams that value a declarative approach to dataflows and want the operational simplicity that comes from automatic dependency management and consistent, reproducible updates.