Streambased for Retail

Bring context to what your shoppers are doing right now

Modern retailers already use Kafka to capture real-time events such as clickstream activity, basket updates, payments and inventory changes, while historical customer behaviour and product demand trends are stored in Iceberg and other analytical systems. The problem is that these systems are architecturally separate, forcing teams to analyse live shopper activity without historical context, or historical behaviour without the latest signals.
Streambased removes that separation. It makes real-time events directly queryable alongside historical data so browsing behaviour, in-store transactions and demand signals can be analysed together in a single view – without copying data or operating ingestion pipelines.

Decisions that previously relied on partial signals can now be made using real-time and historical information together.

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The retail challenge:  

When shopper behaviour moves faster than your data

Retail generates high-volume, high-velocity streams of operational data continuously. Clickstream events, basket updates, payments, store transactions and inventory movements flow through Kafka in real time.

At the same time, years of customer purchase history, product demand trends, pricing performance and supply chain 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 customer behaviour and business response.

This raises multiple challenges:
Fraud detection models built on historical purchase patterns struggle to recognise suspicious checkout activity without visibility of the shopper’s current session.
Demand spikes emerge during a promotion, but historical sales baselines cannot explain whether the surge reflects a real trend without access to live basket activity.
Personalisation engines trained on historical behaviour fail to adapt when a customer suddenly changes browsing patterns during a session.
Inventory planning models built on weeks of demand history cannot react to sudden store-level sales spikes without visibility of live transactions.
Dynamic pricing strategies derived from historical elasticity risk misfiring when they ignore the latest signals from shoppers in the moment.
Supply chain optimisation based on historical demand patterns loses meaning without understanding current store sell-through rates.
The problem isn’t that retailers lack data. It’s that the systems holding live shopper signals and historical 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 the opportunity to respond has passed.

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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 inventory, pricing and customer experience are made against complete and consistent data.

The Streambased solution:

Certainty, control, visibility

Certainty
Certainty icon
Control
Control icon
Visibility
Visibility icon
Certainty:
Fraud detection and demand clarity
When operational decisions rely on partial data, even well-designed models can misfire. With Streambased, systems evaluating checkout transactions, demand spikes or service disruptions can analyse live signals alongside years of behavioural and operational history. Fraud detection becomes more reliable. Demand signals can be interpreted accurately. Customer behaviour shifts can be recognised immediately. Decisions that previously relied on incomplete information can now be made with full context.

What becomes possible:

Customer behaviour with full context: Analyse live shopper activity alongside years of purchase history rather than relying only on warehouse snapshots.
Demand signals with historical baselines: Detect emerging product demand by combining real-time basket activity with long-term sales patterns.
More reliable analytics models: Train and evaluate ML models using datasets that include both live operational signals and historical behaviour.
Faster insight cycles: Analysts can investigate emerging trends immediately instead of waiting for data pipelines to refresh the warehouse.
Control:
Pricing and inventory agility
Retail environments change quickly: promotions trigger unexpected demand, inventory moves across stores, supply chains react to disruption.

By exposing live operational events and historical performance together, Streambased allows pricing engines, inventory systems and operational dashboards to react to changing conditions in real time.

Retailers gain the ability to adjust pricing strategies, rebalance inventory or intervene in customer journeys while the events are still unfolding.

What becomes possible:

Demand-aware pricing strategies: Pricing models can incorporate live purchase signals alongside historical demand elasticity.
Promotion optimisation: Marketing and merchandising teams can evaluate campaign performance using real-time shopper behaviour combined with historical outcomes.
Inventory response to live demand: Supply-chain systems can react to emerging sales patterns using both current transactions and historical demand forecasts.
Operational decision loops: Retail teams can adjust merchandising, promotions or fulfilment strategies as demand patterns evolve.
Real-time customer engagement: Trigger offers and journeys based on live shopper behaviour combined with historical purchase and loyalty data.
Visibility:
Total customer view
Retail operations span multiple systems: ecommerce platforms, point-of-sale systems, logistics networks and customer engagement tools.

Streambased provides a unified analytical view across these systems by allowing queries to span real-time events in Kafka and historical data in Iceberg.

Teams gain a complete timeline of customer behaviour, product demand and operational activity, from the most recent signal back through years of historical context.

What becomes possible:

Continuous retail analytics: Query the latest shopper signals together with years of historical data as a single analytical dataset.
Unified demand timelines: Understand how current demand compares with historical patterns across seasons, promotions and regions.
Omnichannel customer view: Analyse shopper journeys across web, mobile and in-store interactions alongside years of purchase and loyalty history.
Faster analytical exploration: Data teams can investigate trends and anomalies 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 shopper signals and historical retail context disappears at query time: a single SQL statement can analyse the latest browsing behaviour together with years of customer and product history, giving analysts, models and operational systems a unified view of retail activity across time.
What this architecture enables:
Instant data availability
Instant data icon
Newly created events in Kafka – clickstream activity, basket updates, checkout transactions, inventory movements and fulfilment events – become instantly queryable in BI tools, demand forecasting models and customer analytics platforms.
Match storage costs to business value
Flexible pricing
Keep the most recent operational events in Kafka for real-time decisioning, while storing years of customer behaviour, product demand history and inventory performance in cost-efficient object storage. Optimise storage for both speed and long-term analytics.
Unified governance
Unified governance icon
Your existing Kafka security model extends naturally across the analytical layer. The same access controls protecting operational data govern analytical queries, ensuring consistent governance across real-time and historical retail workloads.
Standard tool compatibility
dashboard icon
Works natively with your existing analytics stack – merchandising dashboards, customer analytics platforms and demand forecasting models – and integrates with tools such as Snowflake, Databricks, Spark and Trino.

The Streambased solution:

Certainty. Control. Visibility.

Certainty:
Fraud detection and revenue assurance
Whether it’s Wangiri scams, SIM box bypass, subscription fraud or interconnect manipulation, the pattern is the same: fraudsters move faster than your data pipelines.

By the time overnight ETL updates your blacklist, attackers have already rotated to new SIMs.

With Streambased, you eliminate the trade-off between speed and accuracy.

You can query live signalling streams (SS7, Diameter, GTP) and CDRs against months of historical behaviour patterns – instantly.
  • Block fraud mid-call: Compare current signalling against historical fraud signatures during call setup – before the connection completes.
  • Dynamic threat intelligence: Fraud detection rules informed by complete historical patterns, not yesterday’s batch.
  • Pattern recognition at scale: Correlate today’s suspicious SIM with its complete activity history across months.
  • Revenue protection: Identify interconnect bypass and premium rate fraud in real-time, not in next month’s reconciliation.

Control:
Reduce Churn and drive up LTV

Marketing systems trigger generic offers based on real-time events alone, such as data cap hit or roaming zone entry, without taking account of deeper customer context.

A customer experiencing 5 dropped calls gets a ‘Buy More Data’ SMS instead of an apology and service credit. A high-value customer is hit with a surprise price increase. Such disconnects are likely to increase churn rates.

With Streambased, combining live signals with complete customer context means you can deliver the right action at the exact right moment.
  • Contextual retention: Real-time triggers (cap hit, roaming entry) informed by complete customer journey and sentiment.
  • Predictive churn prevention: Usage anomalies analysed against years of behaviour to identify early churn signals.
  • Service recovery: Network quality issues (dropped calls, slow data) automatically trigger personalised retention offers.
  • Lifetime value optimisation: Instant access to customer profitability to inform real-time offer decisioning.

Visibility:
Network optimisation and asset performance

When an alarm fires at 3am for equipment failure on Cell Tower #4732, the engineer needs to know: Hardware fault? Configuration drift? Capacity overload? Is this tower failure a random event or part of a degradation pattern emerging over weeks?Historical performance data sits in the data warehouse, requiring ETL lag to access. By the time patterns are identified, service has been degraded for hours.

Streambased gives you total visibility into infrastructure and network performance.

Use it to retain and query 100% of network telemetry – RAN metrics, core KPIs, transport performance and OSS logs – without the prohibitive cost of traditional real-time indexing.
  • 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.

How Streambased benefits your

business roles
Streambased can power better intelligence for your entire retail business.

CIO/CTO/Chief Data Officer

Turn your data infrastructure into competitive advantage

Transform operational data from fragmented systems into a unified decision layer. Eliminate ETL overhead between e-commerce platforms, warehouses and analytics systems. Enable faster merchandising decisions, supply chain optimisation and AI-driven customer experiences while reducing infrastructure complexity.

Single source of truth across stores, online channels and fulfilment systems.
Faster decision cycles for pricing, promotions and inventory planning.
AI and forecasting models trained on complete historical and real-time data.
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.

Chief Marketing & Loyalty

Turn every interaction into a context-aware engagement

Combine live shopper behaviour across digital and in-store channels with years of customer and campaign history to power real-time personalisation, offers and retention strategies.

Trigger journeys based on live behaviour with full historical context.
Measure campaign and promotion impact in real time, not next week.
Detect churn signals as they emerge, not after the fact.

Merchandising & Commercial Analytics

From overnight reports to live decision-making

Stop waiting for nightly batch jobs to understand what customers are buying. Analyse sales patterns across years of history while reacting instantly to live store and online activity.
Analyse sales performance across real-time and historical transactions in one query.
Detect demand trends and promotion impact instantly.
Run pricing and assortment experiments with immediate feedback.
Combine clickstream, orders and inventory signals for full commercial context.

Supply Chain & Operations

End-to-end visibility across the retail network

Track inventory movement, logistics events and fulfilment performance in real time while analysing historical trends across months or years.
Unified visibility into stock levels across warehouses and stores.
Identify supply chain disruptions immediately.
Analyse historical fulfilment performance alongside live operational signals.
Optimise replenishment and logistics decisions using complete context.

Fraud & Customer Trust

Stop fraud before it spreads

Detect fraudulent activity across payments, returns and account behaviour by combining years of transaction history with real-time customer activity.
Identify suspicious behaviour patterns across historical purchase data.
Analyse live payment activity alongside customer history.
Detect return abuse and account takeovers faster.
Reduce false positives through full behavioural context.

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

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Try it out