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.
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
Certainty: Predictive maintenance and supply-chain reliability
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.
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.
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.
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.
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.
How Streambased benefits your business roles
Streambased can power better intelligence for your entire Logistics and Manufacturing business.
CIO/CTO/Chief Data Officer
Turn your data infrastructure into competitive advantage
Transform fragmented operational systems into a unified decision layer across factories, logistics networks and supply chains. Eliminate ETL overhead between operational platforms and analytical environments while enabling faster insight into production, reliability and supply-chain performance.
- Single source of truth across manufacturing plants, logistics networks and operational systems.
- Faster decision cycles for production optimisation and supply-chain planning.
- AI models trained on both real-time operational signals and years of performance history.
- Future-proof architecture built on open standards (Kafka + Iceberg).
Platform & Engineering
Simplify the stack, free up the team
Remove the need to continuously copy streaming data into analytical systems. Streambased exposes Kafka data directly as Iceberg tables, eliminating fragile ingestion pipelines and reducing infrastructure overhead. Engineering teams spend less time maintaining data movement and more time building real capabilities.
- No connector fan-out pushing the same data into multiple systems.
- Kafka data becomes instantly queryable as Iceberg tables without copying it.
- Reduced infrastructure costs by eliminating duplicated storage and pipelines.
- Engineering teams focus on building products instead of maintaining data plumbing.
Production & Industrial Engineering
Understand production behaviour as it happens
Analyse machine telemetry and production activity across years of operational history while investigating emerging issues in real time.
- Analyse machine signals and production output across real-time and historical datasets.
- Detect production anomalies faster using historical performance baselines.
- Investigate process deviations using both live operational signals and historical production data.
- Improve production efficiency through deeper operational insight.
Supply Chain & Logistics
End-to-end visibility across the supply network
Track shipments, warehouse activity and logistics operations in real time while analysing historical supply chain performance.
- Unified visibility across warehouses, transport networks and distribution systems.
- Identify supply chain disruptions earlier using real-time operational signals.
- Analyse logistics performance alongside live shipment activity.
- Optimise planning decisions using complete operational context.
Quality, Maintenance & System Improvement
Detect issues before they impact production
Monitor machine behaviour and product quality signals while analysing historical defect patterns and equipment performance.
- Identify emerging equipment issues using live telemetry and historical performance.
- Investigate production defects using complete operational context.
- Improve predictive maintenance models with richer datasets.
- Reduce downtime and quality issues through earlier insight.
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