Streambased for Healthtech
Bring context to what your operations and safety systems are telling you right now
Modern healthtech and life sciences organisations already use Kafka to capture real-time operational events such as patient monitoring data, medical device telemetry, clinical workflow events, manufacturing sensor data and supply chain signals. Meanwhile, years of clinical records, device history, treatment outcomes, quality data and safety reports are stored in Iceberg and analytical platforms.
The problem is that these systems are architecturally separate, forcing teams to analyse live signals without historical context, or historical performance without the latest data from patients, devices and operations.
Streambased removes that separation. It makes real-time clinical, operational and safety events directly queryable alongside historical data, so patient activity, device behaviour, clinical outcomes and operational performance can be analysed together in a single view, without copying data or operating ingestion pipelines. Decisions that previously relied on partial signals – across care delivery, safety, operations and compliance – can now be made using real-time and historical information together.
Streambased removes that separation. It makes real-time clinical, operational and safety events directly queryable alongside historical data, so patient activity, device behaviour, clinical outcomes and operational performance can be analysed together in a single view, without copying data or operating ingestion pipelines. Decisions that previously relied on partial signals – across care delivery, safety, operations and compliance – can now be made using real-time and historical information together.
The Healthtech challenge:
When care, safety and operations move faster than your data
Healthtech platforms, providers and life sciences organisations generate continuous streams of high-stakes data: patient monitoring signals, device telemetry, clinical events, manufacturing data and logistics activity flow through Kafka in real time.
At the same time, years of clinical records, treatment history, device performance data, safety reports and operational data live in Iceberg and downstream analytical platforms.
But these two worlds remain disconnected, linked only by slow, expensive ETL pipelines that create critical gaps between live signals and the decisions that depend on them.
At the same time, years of clinical records, treatment history, device performance data, safety reports and operational data live in Iceberg and downstream analytical platforms.
But these two worlds remain disconnected, linked only by slow, expensive ETL pipelines that create critical gaps between live signals and the decisions that depend on them.
This creates systemic challenges:
Clinical and safety teams cannot correlate incoming events with full patient, device or treatment history during critical decision windows.
Operational systems detect anomalies in real time but cannot immediately compare them with full historical baselines.
Logistics and device monitoring systems generate continuous alerts, but historical performance and tolerance data are not available when decisions must be made.
Regulatory and audit workflows require reproducible, time-consistent data, but analytical systems lag behind operational reality.
Operational disruptions appear in real time, but planning and optimisation systems cannot react due to separation between live and historical data.
The issue is not a lack of data. It’s that real-time signals and historical context were never designed to work together at the moment decisions are made. Connected only through batch pipelines, the context needed to interpret live activity often arrives too late – after clinical, operational or safety windows have passed.
Streambased removes the trade-off between speed and context by making real-time and historical data accessible together in a single, queryable view. Decisions across clinical care, safety, operations and supply chain can now be made with complete and consistent data, without copying data or relying on ingestion pipelines.
The Streambased solution:
Certainty, control, visibility
When clinical, safety and operational decisions rely on partial data, even well-designed detection models can miss signals or misinterpret anomalies. In healthtech environments, the gap between incoming real-time events and historical context creates risks across patient safety, operational performance and regulatory compliance.
A missed safety signal can allow an issue to propagate across patients or devices before action is taken. A misinterpreted operational anomaly can lead to incorrect clinical decisions, delayed interventions or unnecessary disruption to care delivery.
With Streambased, systems processing real-time events – whether patient signals, device telemetry or safety reports – can correlate new data with full historical context, prior outcomes and operational records in a single query. Decisions that previously required manual data assembly across multiple systems can now be made with complete context at the moment they emerge.
A missed safety signal can allow an issue to propagate across patients or devices before action is taken. A misinterpreted operational anomaly can lead to incorrect clinical decisions, delayed interventions or unnecessary disruption to care delivery.
With Streambased, systems processing real-time events – whether patient signals, device telemetry or safety reports – can correlate new data with full historical context, prior outcomes and operational records in a single query. Decisions that previously required manual data assembly across multiple systems can now be made with complete context at the moment they emerge.
What becomes possible:
Safety events with full historical context: Correlate incoming case reports with complete case history, prior adverse events and product lot data during the initial assessment window.
Operational anomalies with historical baselines: Determine whether an out-of-tolerance condition represents an isolated event or a developing pattern by comparing live sensor data with years of batch and equipment performance history.
More reliable detection models: Train and evaluate clinical, safety and operational models using datasets that combine incoming case data with complete historical case and production records.
Faster response to emerging issues: Safety and quality teams can investigate emerging signals immediately instead of waiting for ETL pipelines to refresh analytical systems.
Healthtech systems operate in environments that change continuously: patient conditions evolve in real time, devices generate continuous telemetry, logistics conditions fluctuate and operational constraints shift across organisations.
By exposing live operational events alongside historical performance data, Streambased enables clinical systems, device platforms, logistics tools and operational applications to interpret operational signals in full context. Release decisions, inventory interventions and logistics adjustments can be made using both the latest operational data and the complete historical record that gives it meaning.
New data sources, devices or schema changes can be incorporated without rebuilding ingestion pipelines. This is particularly valuable in healthtech ecosystems, where data originates from distributed environments – hospitals, devices, partners and third-party providers. Streambased allows data from across this network to be queried alongside historical performance records without requiring a separate integration project for every new source or supplier relationship.
By exposing live operational events alongside historical performance data, Streambased enables clinical systems, device platforms, logistics tools and operational applications to interpret operational signals in full context. Release decisions, inventory interventions and logistics adjustments can be made using both the latest operational data and the complete historical record that gives it meaning.
New data sources, devices or schema changes can be incorporated without rebuilding ingestion pipelines. This is particularly valuable in healthtech ecosystems, where data originates from distributed environments – hospitals, devices, partners and third-party providers. Streambased allows data from across this network to be queried alongside historical performance records without requiring a separate integration project for every new source or supplier relationship.
What becomes possible:
Real-time release and intervention decisions with full context: Teams can evaluate live alerts against complete historical performance, environmental conditions and compliance thresholds in a single query without assembling data from multiple systems.
Operational disruption response: Teams can interpret live disruptions using both real-time signals and historical performance across systems, partners and environments, enabling faster intervention.
Demand-aware resource allocation: Systems can respond to emerging demand signals using live activity data alongside historical usage patterns and baselines.
Faster operational decision cycles: Teams can query real-time data alongside complete historical records, reducing the time required to assemble the data needed for critical decisions.
Healthtech environments span multiple specialised systems: clinical platforms, device systems, operational applications, monitoring tools and compliance systems.
Each system captures a different part of operational and safety activity, but the signals they generate are rarely analysed together with the historical performance data stored in analytical platforms. The result is a fragmented operational picture that complicates regulatory reporting, audit readiness and cross-functional insight.
Streambased provides a unified analytical view across these environments by allowing queries to span real-time operational events in Kafka and historical data in Iceberg. Teams gain a complete, time-consistent record of clinical activity, operational performance and safety signals, from the most recent operational event back through years of historical context.
Each system captures a different part of operational and safety activity, but the signals they generate are rarely analysed together with the historical performance data stored in analytical platforms. The result is a fragmented operational picture that complicates regulatory reporting, audit readiness and cross-functional insight.
Streambased provides a unified analytical view across these environments by allowing queries to span real-time operational events in Kafka and historical data in Iceberg. Teams gain a complete, time-consistent record of clinical activity, operational performance and safety signals, from the most recent operational event back through years of historical context.
What becomes possible:
Continuous safety monitoring: Query incoming events together with years of historical records, prior incidents and outcomes as a single analytical dataset.
Unified operational timelines: Understand how current activity compares with historical performance across systems, devices and environments.
Audit-ready data: Reproduce the exact state of operational and safety data at any historical point in time, supporting compliance, reporting and investigation without manual data assembly.
End-to-end operational visibility: Analyse events across systems, devices and logistics networks with both live and historical context.
Faster investigation: Teams can explore anomalies and deviations immediately instead of waiting for scheduled ETL refresh cycles.
Zero-copy architecture
for unified access to Kafka and Iceberg
Streambased turns Kafka from a write-only streaming backbone into a directly queryable analytical data source. By exposing Kafka topics as Iceberg-compatible tables and stitching them with existing Iceberg history, Streambased gives query engines a single logical view across real-time and historical data, without continuously copying data or running ingestion pipelines.
Streambased sits alongside your existing platforms, complementing current ETL processes. The boundary between live operational signals and historical records disappears at query time: a single SQL statement can analyse the latest patient signals, device telemetry or operational events alongside years of batch records, stability data and quality event records.
Streambased sits alongside your existing platforms, complementing current ETL processes. The boundary between live operational signals and historical records disappears at query time: a single SQL statement can analyse the latest patient signals, device telemetry or operational events alongside years of batch records, stability data and quality event records.
What this architecture enables:
Instant data availability
Newly created events in Kafka – patient signals, device telemetry, operational events – become instantly queryable in pharmacovigilance platforms, quality management systems and regulatory reporting tools.
Match storage costs to business value
Kafka is designed for real-time processing, not long-term storage. Streambased allows organisations to minimise Kafka retention while offloading historical records to cost-efficient object storage. Complete batch records, stability data and case histories remain accessible to both analytical queries and operational systems, without the cost of holding years of data inside Kafka.
Unified governance
Healthtech data is subject to strict regulatory requirements around access control, audit trails and data integrity. Streambased extends your existing Kafka governance model to the analytical layer, allowing the same access controls protecting live operational streams to apply when those events are queried alongside historical records. This supports GxP compliance without introducing additional data copies or separate governance overhead.
Standard tool compatibility
Streambased integrates with your existing analytical and regulatory reporting stack. By exposing Kafka topics as Iceberg-compatible tables, operational and safety events become queryable using standard Iceberg-compatible engines such as Spark and Trino, as well as platforms like Snowflake and Databricks that are used for quality analytics and regulatory intelligence.
Talk to us
about your data stack
We'd love to learn about your operation and show you how a unified, instantly queryable view of your hot and cold data can drive measurable outcomes
