Streambased for
Logistics & Manufacturing
Bring context to what your operations are doing right now
Modern logistics and manufacturing platforms already use Kafka to capture real-time operational events such as machine telemetry, production line activity, shipment updates and warehouse movements. Meanwhile, years of operational history, maintenance records and supply chain performance data are stored in Iceberg and analytical platforms.
The problem is that these systems are architecturally separate, forcing teams to analyse live operational activity without historical context, or historical performance without the latest signals from machines, vehicles and supply chains.
Streambased removes that separation. It makes real-time operational events directly queryable alongside historical data, so machine behaviour, production output, logistics movements and maintenance history can be analysed together in a single view, without copying data or operating ingestion pipelines.
Operational decisions that previously relied on partial signals can now be made using real-time and historical information together.
The Logistics & Manufacturing challenge
When operations move faster than your data
Manufacturing plants and logistics networks generate continuous streams of operational data. Machine telemetry, sensor readings, production line events, shipment updates and warehouse movements flow through Kafka in real time.
At the same time, years of equipment performance history, production metrics, maintenance logs and supply-chain performance data live in Iceberg and downstream analytics platforms.
But these two worlds remain disconnected, linked only by slow, expensive ETL pipelines that create critical gaps between operational signals and the decisions that depend on them.
At the same time, years of equipment performance history, production metrics, maintenance logs and supply-chain performance data live in Iceberg and downstream analytics platforms.
But these two worlds remain disconnected, linked only by slow, expensive ETL pipelines that create critical gaps between operational signals and the decisions that depend on them.
This raises multiple challenges:
Operational dashboards show real-time activity but cannot explain how current performance compares to historical production baselines.
New operational data takes time to become usable, forcing teams to operate on incomplete datasets.
Predictive maintenance models rely on historical failure and real-time sensor signals that are processed through different systems and formats, forcing teams to rebuild the same data integration every time a model is developed or updated.
Production anomalies appear on the factory floor, but historical performance baselines cannot explain whether the deviation is meaningful without access to live telemetry.
Supply chain optimisation models built on historical shipment data fail to react when logistics disruptions appear in real-time tracking feeds.
Warehouse automation systems process operational events without understanding historical throughput patterns or demand cycles.
The problem isn’t that manufacturers and logistics providers lack data. It’s that the systems holding live operational signals and historical performance context were never designed to work together in the moment decisions are made. Connected only through batch pipelines, the context needed to interpret real-time activity often arrives long after operational disruptions have occurred.
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 production, supply chain and operations are made against complete and consistent data, without copying data or relying on ingestion pipelines.
The Streambased solution:
Certainty, control, visibility
When operational decisions rely on partial data, even well-designed optimisation models can misinterpret events.
With Streambased, systems evaluating machine behaviour, logistics disruptions or production anomalies can analyse live operational signals alongside years of performance history.
Predictive maintenance becomes more reliable. Production anomalies can be interpreted accurately. Supply chain disruptions can be detected earlier.
Operational signals that were previously interpreted in isolation can now be analysed with full historical context.
With Streambased, systems evaluating machine behaviour, logistics disruptions or production anomalies can analyse live operational signals alongside years of performance history.
Predictive maintenance becomes more reliable. Production anomalies can be interpreted accurately. Supply chain disruptions can be detected earlier.
Operational signals that were previously interpreted in isolation can now be analysed with full historical context.
What becomes possible:
Machine behaviour with full context: Analyse live telemetry alongside years of equipment performance and maintenance history.
Production anomalies with historical baselines: Detect abnormal machine behaviour by comparing real-time sensor signals with long-term performance patterns.
More reliable analytics models: Train and evaluate maintenance and optimisation models using datasets that include both live operational signals and historical performance.
Faster operational insight cycles: Engineers and operations teams can investigate emerging issues immediately instead of waiting for data pipelines to update analytical systems.
Manufacturing environments and logistics networks change constantly: machines drift out of tolerance, production lines slow down, shipments are delayed and supply chains react to disruption.
By exposing live operational events alongside historical performance, Streambased allows monitoring systems, optimisation models and analytical tools to interpret operational signals in context.
Operations and engineering teams gain the ability to understand what is happening across factories and supply chains in real time, using the same historical context that previously existed only in offline analytical systems.
In addition, new operational signals, data sources or schema changes can be incorporated without rebuilding ingestion pipelines, allowing systems and models to evolve without introducing additional data movement or engineering overhead.
By exposing live operational events alongside historical performance, Streambased allows monitoring systems, optimisation models and analytical tools to interpret operational signals in context.
Operations and engineering teams gain the ability to understand what is happening across factories and supply chains in real time, using the same historical context that previously existed only in offline analytical systems.
In addition, new operational signals, data sources or schema changes can be incorporated without rebuilding ingestion pipelines, allowing systems and models to evolve without introducing additional data movement or engineering overhead.
What becomes possible:
Predictive maintenance with full context: Maintenance systems can analyse live machine telemetry alongside years of equipment performance and failure history.
Production anomalies understood faster: Engineers can investigate unusual production behaviour using both real-time machine signals and historical performance baselines – without building pipelines or custom integration to combine them.
Supply-chain disruption analysis: Logistics teams can interpret shipment delays or warehouse bottlenecks using both live operational signals and historical logistics performance.
Faster operational investigation: Analysts and engineers can explore emerging operational issues immediately instead of waiting for ETL pipelines to refresh analytical systems.
Manufacturing plants and logistics networks rely on many specialised systems: industrial IoT platforms, production monitoring tools, warehouse systems and transport management platforms.
Each system captures a different part of operational activity, but the signals they generate are rarely analysed together with the historical performance data stored in analytical platforms.
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 timeline of machine behaviour, production output and logistics activity, from the most recent operational signal back through years of historical context.
Each system captures a different part of operational activity, but the signals they generate are rarely analysed together with the historical performance data stored in analytical platforms.
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 timeline of machine behaviour, production output and logistics activity, from the most recent operational signal back through years of historical context.
What becomes possible:
Continuous operational analytics: Query live machine telemetry together with years of equipment performance and maintenance history.
Unified demand timelines: Understand how current production behaviour compares with historical output and efficiency patterns.
End-to-end supply chain visibility: Analyse logistics activity across warehouses, transport networks and distribution systems with both live and historical context.
Faster operational investigation: Engineers and analysts can explore anomalies and disruptions 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 warehouse, complementing current ETL processes. The boundary between live operational signals and historical operational context disappears at query time: a single SQL statement can analyse the latest machine telemetry, production events or shipment updates together with years of equipment performance, production history and supply chain data.
Streambased sits alongside your existing warehouse, complementing current ETL processes. The boundary between live operational signals and historical operational context disappears at query time: a single SQL statement can analyse the latest machine telemetry, production events or shipment updates together with years of equipment performance, production history and supply chain data.
What this architecture enables:
Unified governance
Operational data becomes harder to govern when it is copied across multiple analytical systems. Each pipeline introduces new security policies, access rules and compliance overhead.
Streambased extends your existing Kafka governance model to the analytical layer, allowing the same access controls protecting operational streams to apply when those events are queried alongside historical data. This keeps governance consistent across real-time and historical workloads without introducing additional data copies.
Streambased extends your existing Kafka governance model to the analytical layer, allowing the same access controls protecting operational streams to apply when those events are queried alongside historical data. This keeps governance consistent across real-time and historical workloads without introducing additional data copies.
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 events to cost-efficient object storage. Operational history remains accessible to both analytical queries and streaming consumers, creating the illusion of a continuous log without the cost of storing weeks of data inside Kafka.
Instant data availability
Newly created events in Kafka - machine telemetry, sensor readings, production line activity, warehouse movements and shipment updates - become instantly queryable in operational dashboards, analytics platforms and reliability monitoring systems.
Standard tool compatibility
Streambased integrates with your existing analytics stack. By exposing Kafka topics as Iceberg-compatible tables, operational events become queryable using standard Iceberg-compatible query engines such as Spark and Trino, as well as analytics platforms like Snowflake and Databricks.
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
