Streambased for Telecom
See network behaviour across time, not across systems
Streambased unlocks the full value of your Kafka architecture by unifying real-time network streams with full historical context. That opens up many powerful possibilities such as blocking fraud mid-call, preventing churn before it happens, and optimising your network in the moment.
Streambased exposes Kafka and Iceberg as different time horizons of the same dataset, allowing operators to relate live signalling, CDRs and telemetry to long-term behavioural patterns as they form.
This removes the need for batch replication pipelines and duplicate storage, enabling teams to investigate anomalies, detect fraud and understand customer experience without waiting for data to stabilise in analytical systems.
Streambased exposes Kafka and Iceberg as different time horizons of the same dataset, allowing operators to relate live signalling, CDRs and telemetry to long-term behavioural patterns as they form.
This removes the need for batch replication pipelines and duplicate storage, enabling teams to investigate anomalies, detect fraud and understand customer experience without waiting for data to stabilise in analytical systems.
With Streambased, the boundary between present and past is no longer an obstacle.
You get the business certainty that comes from a single, unified view of your complete data universe where live CDRs meet years of billing history, real-time signalling informs historical fraud patterns and every network event gains instant context.
Let us show you what Streambased can do
Get a demo and discover the impact of Streambased on your business.
The telecoms challenge:
Real-time decisions blocked by ETL lag
Telecom networks generate high-volume, high-velocity data continuously. CDRs, signalling events, network telemetry, and customer usage flow through Kafka in real time.
At the same time, years of billing history, fraud patterns, and network performance baselines 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 insight and action.
At the same time, years of billing history, fraud patterns, and network performance baselines 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 insight and action.
Fraud intelligence built on past patterns quickly becomes unreliable without awareness of emerging attacker behaviour.
Network congestion emerges, but historical baselines can't explain whether it's abnormal without sight of current traffic context.
Churn models built on months of behaviour miss sudden shifts without visibility of most recent customer interactions.
Long-term equipment reliability analysis cannot predict imminent failure without awareness of the latest alarm signals.
Retention strategies derived from historical behaviour risk misfiring when they ignore the customer’s current experience.
Network capacity planning based on historical traffic patterns loses meaning without understanding present load conditions.
The challenge is not data availability, but timing and architecture. These two worlds are typically connected by ETL pipelines that were designed for batch analytics, not real-time operational decisions. As a result, the contextual data needed to interpret live network events often arrives minutes or hours too late.
Close the gap between what just happened and all that came before
By treating Kafka and Iceberg as different time horizons of the same dataset, Streambased enables analytical applications to operate without temporal blind spots.
The Streambased solution:
Certainty, control, visibility
Wangiri scams, SIM box bypass, subscription fraud, interconnect manipulation... attackers evolve behaviour faster than analytical views adapt. Blacklists and fraud models built on historical activity struggle to explain emerging patterns without visibility into live signalling.
Fraud patterns rarely appear in live streams or historical data alone: they emerge when both are observed together. Streambased enables just that.
Teams can correlate live signalling streams (SS7, Diameter, GTP) and CDRs with months of behavioural history as a single dataset, allowing emerging patterns to be recognised as they form rather than after they stabilise.
Fraud patterns rarely appear in live streams or historical data alone: they emerge when both are observed together. Streambased enables just that.
Teams can correlate live signalling streams (SS7, Diameter, GTP) and CDRs with months of behavioural history as a single dataset, allowing emerging patterns to be recognised as they form rather than after they stabilise.
What becomes possible: Holistic view of emerging fraud patterns
Block fraud mid-call: Evaluate live signalling against the subscriber’s behavioural history during call setup - before the connection completes.
Behaviour-aware detection: Fraud rules operate on current activity and full historical context simultaneously, not delayed analytical views.
Subscriber-level correlation: Interpret suspicious SIM activity by immediately relating it to its long-term usage patterns.
Real-time revenue protection: Detect interconnect bypass and premium-rate abuse as behaviour emerges, not during reconciliation cycles.
Marketing systems often react to isolated live events, such as roaming entry or data cap notifications, without awareness of the customer’s broader experience and value history.
A customer experiencing repeated dropped calls receives a “Buy More Data” message instead of acknowledgement and service recovery. A high-value subscriber facing a sudden price change disconnects quietly, with no signal strong enough to trigger intervention.
Streambased allows live experience signals to be interpreted alongside long-term customer context, so retention actions reflect what the customer is experiencing now,not just what their profile suggests.
A customer experiencing repeated dropped calls receives a “Buy More Data” message instead of acknowledgement and service recovery. A high-value subscriber facing a sudden price change disconnects quietly, with no signal strong enough to trigger intervention.
Streambased allows live experience signals to be interpreted alongside long-term customer context, so retention actions reflect what the customer is experiencing now,not just what their profile suggests.
What becomes possible: Total view of customer behaviour across time
Context-aware retention: Interpret real-time triggers (cap hit, roaming entry) in the context of the customer’s full journey and recent experience.
Early churn detection: Identify behavioural shifts by relating today's anomalies to long-term usage patterns.
Experience-driven recovery: Use live network quality signals (dropped calls, slow data) together with customer value to trigger appropriate intervention.
Informed offer decisions: Evaluate customer profitability and current behaviour together when determining retention actions.
When a customer enters a roaming zone, Streambased instantly queries their complete roaming history, recent network quality issues, lifetime value and contract status. High-value customers get personalised offers preventing bill shock; price-sensitive roamers see standard rates. Every trigger is informed by complete customer context, such as purchase history, service quality metrics and sentiment indicators.
When an alarm fires at 3am on Cell Tower #4732, the engineer isn’t just asking what happened, but whether the event reflects a transient spike, a configuration issue or a degradation pattern developing over weeks.
Historical performance explains long-term behaviour, while live telemetry reveals current stress. Yet these perspectives are often accessed separately, forcing operators to reason with incomplete context.
Streambased allows infrastructure behaviour to be interpreted across both time horizons simultaneously, so anomalies can be understood as they develop rather than after service degradation becomes obvious.
Historical performance explains long-term behaviour, while live telemetry reveals current stress. Yet these perspectives are often accessed separately, forcing operators to reason with incomplete context.
Streambased allows infrastructure behaviour to be interpreted across both time horizons simultaneously, so anomalies can be understood as they develop rather than after service degradation becomes obvious.
What becomes possible:
Reduced MTTR (mean time to repair) through instant pattern recognition.
Lower infrastructure costs through predictive maintenance.
Improved network quality and customer experience.
Optimized CapEx through data-driven capacity planning.
Zero-copy architecture
for unified access to Kafka and Iceberg
Streambased turns Kafka from awrite-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 hot and cold data becomes invisible to your queries: one SQL statement seamlessly returns both real-time data and years of historical records, creating a single source of truth for network operations, customer analytics and financial reporting.
Streambased sits alongside your existing warehouse, complementing current ETL processes. The boundary between hot and cold data becomes invisible to your queries: one SQL statement seamlessly returns both real-time data and years of historical records, creating a single source of truth for network operations, customer analytics and financial reporting.
This raises multiple challenges:
Instant data availability
New Kafka topics become instantly queryable in your BI tools, data science platforms and fraud detection systems.
Flexible retention economics
Balance Kafka costs vs. performance needs. Keep 3 days hot for fraud detection, 7 days for operations, historic data in cost-effective Iceberg storage – you choose.
Unified governance
Your Kafka ACLs, schemas and access controls automatically apply to analytics queries, creating a single security model across operational and analytical data.
Standard tool compatibility
Plugs easily into Tableau, PowerBI, Snowflake, Databricks, Spark, Trino – anything that speaks Iceberg.
What becomes possible?
Root cause analysis
Live alarms enriched with complete performance history enable ‘time travel’ debugging and pattern recognition, to distinguish isolated incidents from systemic issues.
Capacity planning
Real-time traffic loads analysed against seasonal/historic patterns to predict congestion before customer impact and inform infrastructure investment planning.
Operational intelligence (AIOps)
Correlate logs and metrics from OSS components (inventory, assurance, orchestration) with network performance to automate incident response and reduce MTTR.
Predictive maintenance
Real-time equipment telemetry compared against historical failure signatures. Vibration patterns, temperature anomalies, performance degradation tracked across months. Maintenance scheduled before critical failures occur.
5G & Open RAN optimisation:
Slice performance monitoring with historical SLA baselines and RAN component optimization informed by weeks of operational metrics. Complete operational data informs vendor benchmarking
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
