Streambased for Finance
Turn financial data into strategic advantage – with no pipeline lag
Most financial institutions already use Kafka for real-time data and Iceberg for historical analytics. The problem is that these systems are architecturally separated, forcing teams to analyse live data without historical context, or historical data without live signals.
Streambased removes that separation. It makes real-time Kafka data directly queryable alongside historical Iceberg data, so market events, payments and trades can be analysed together in a single, consistent view, without copying data or running ingestion pipelines.
Decisions that previously relied on partial views of the data can now be made using real-time and historical information together, with full context and confidence.
The FS challenge
Accessing real-time and historic data together
Your trading floors, payment processors and risk engines run on speed and generate massive data volumes. Market ticks, card authorizations and trade executions stream through Kafka every millisecond, while your strategic assets (transaction histories, customer profiles, compliance audit trails, quantitative models) accumulate in Iceberg for deep analysis.
But these critical data worlds operate in isolation, typically connected by batch ETL pipelines designed to move data between systems rather than make real-time and historical data accessible together.
This raises multiple challenges:
- Alpha opportunities vanish as strategies can't be evaluated against live tick data and full historical context at the same time.
- Fraud rings evolve overnight while threat models operate on incomplete behavioural history during authorisation.
- Intraday risk exposure remains unclear because live positions cannot be analysed together with accumulated exposure.
- Market manipulation goes undetected as surveillance relies on historical patterns disconnected from current order flow.
- Regulatory reports are built from snapshots rather than current, consolidated positions.
The challenge is not data availability, but architecture. Real-time and historical data live in separate systems and cannot be accessed together in a single, consistent view. ETL pipelines move data between these systems, but they do not make that data queryable together at decision time.
As a result, the price volatility context quants need to validate algorithms is separated from live market data. The transaction patterns fraud teams need to catch account takeovers are inaccessible during authorisation. The exposure data risk officers need for intraday P&L cannot be analysed together with current positions.
Meanwhile, duplicated data across systems leads to reconciliation nightmares and inconsistent positions across trading, risk and compliance. While high-frequency traders measure success in microseconds, decisions made on fragmented data translate directly into missed opportunities, undetected fraud and unmanaged risk.
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 trading, risk and compliance are made against complete and consistent data, without copying data or relying on ingestion pipelines.
The Streambased solution
Certainty, control, visibility
Certainty: Fraud detection and market risk
Whether it's payment fraud, account takeovers, money laundering or market manipulations such as spoofing and layering, the pattern is the same: threats exploit gaps in context. Real-time events are evaluated without access to full historical behaviour, while historical analysis is disconnected from live activity.
Streambased eliminates this trade-off between speed and accuracy. Use it to query live payment streams and order books against years of historical transaction patterns and tick data in an instant.
What becomes possible
- Block fraud mid-transaction: Compare current payment activity against complete customer transaction history during authorisation, before settlement completes.
- Dynamic threat intelligence: Fraud detection models informed by complete historical patterns, not just yesterday's batch.
- Pattern recognition at scale: Correlate today's suspicious activity with months of behavioural data across accounts and channels.
- Market surveillance: Identify spoofing, layering and wash trading in real time by comparing live order flow against historical manipulation signatures.
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. Your trading floors, payment processors and risk engines run on speed and generate massive data volumes. Market ticks, card authorisations and trade executions stream through Kafka every millisecond, while your strategic assets (transaction histories, customer profiles, compliance audit trails, quantitative models) accumulate in Iceberg for deep analysis.
Streambased sits alongside your existing warehouse, complementing your existing ETL processes. The distinction between streaming ticks and historical archives vanishes: one query can instantly span both your real-time order flow and years of market data, creating a single source of truth for all trading, risk and regulatory reporting.
What this architecture enables
Instant data availability
New Kafka topics (payment streams, market feeds, trading events) become instantly queryable in your BI tools, risk platforms, and fraud detection systems.
Match storage costs to business value
For example, keep 7 days of tick data in Kafka for lightning-fast backtesting, 30 days for regulatory snapshots, decades of transaction history in cost-efficient Iceberg. Optimise for both speed and compliance requirements.
Unified governance
Your existing Kafka security model extends seamlessly – the same ACLs protecting live trading feeds automatically govern historical queries, ensuring consistent compliance across operational and analytical workloads.
Standard tool compatibility
Works natively with your quantitative research platforms, risk dashboards, compliance tools and other ecosystem components such as Tableau, PowerBI, Snowflake, Databricks, Spark and Trino.
How Streambased benefits your business roles
Leadership
CIO/CTO/Chief Data Officer
Turn data infrastructure into competitive advantage
Transform regulatory storage from cost centre to alpha engine. Eliminate ETL overhead, accelerate decision-making from hours to milliseconds, and enable AI-driven trading and risk operations. All this while reducing storage costs and maintaining compliance with open standards architecture.
- Single source of truth eliminates synchronisation issues across trading, risk and compliance systems.
- Simplified architecture improves regulatory compliance and security.
- Real-time AI/ML models for fraud detection and trading informed by complete historical context.
- Future-proof architecture built on open standards (Kafka + Iceberg).
Platform & Engineering
Simplify the stack, free up the team
Erase the complexity of maintaining fragile ETL pipelines between trading engines, payment systems and data lakes. No more 3am failures when market data feeds break. No weeks-long pipeline development for new data sources. Focus engineering resources on alpha generation and fraud prevention instead.
- Query optimisation and schema evolution handled automatically.
- New market data feeds and payment streams queryable instantly, not after weeks of development.
- Self-service analytics for quants and risk teams reduces support burden.
- More time for strategic projects like ML-driven trading algorithms and fraud models.
Trading Desks & Quantitative Research
From backtesting lag to real-time alpha
Stop waiting for overnight batch loads to validate strategies. Test algorithms against years of tick data in milliseconds. Adjust positions mid-session based on live pattern matching against historical volatility.
- Backtest strategies against complete market histories in milliseconds, not hours.
- Simplified architecture improves compliance and security.
- Deploy new algorithms with confidence based on comprehensive validation.
- Turn operational intelligence into alpha generation through instant strategy validation.
Risk & Compliance
Proactive risk management
Move from overnight risk reports to continuous intraday visibility. Respond to regulatory requests in minutes, not days. Eliminate the gap between actual exposure and reported positions.
- Intraday visibility into consolidated positions across all desks and products.
- Time-travel queries reproduce exact market state at any moment for regulatory audits.
- T+0 reporting capabilities for immediate compliance with evolving regulations.
- Reduced regulatory penalties through faster, more accurate reporting.
Fraud, Security & AML
Stop threats before they compound
Block fraudulent transactions during authorisation, not after settlement. Detect money-laundering patterns spanning years in real time. Dynamic threat intelligence continuously updated from historic transaction behaviour gives you complete customer profiles in milliseconds.
- Detect AML patterns across years of transaction data – queryable in milliseconds.
- Compare live payment activity against years of customer behaviour to detect account takeovers.
- Reduce false positives through complete behavioural context.
- Protect revenue and regulatory standing through real-time threat response.
Talk to us about your data stack
We'd love to have a chat about data in your financial services operation and show you how a unified, instantly queryable view of hot and cold data can drive measurable outcomes.