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.
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.
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
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.
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.
Trading platforms demand millisecond execution while instant payment rails require immediate fraud validation – but accessing the historical context needed for confident decision-making introduces unacceptable latency. Traditional batch processing creates bottlenecks during peak trading periods and payment surges.
With Streambased, combining live execution with complete historical validation means you can act fast with full confidence.
With Streambased, combining live execution with complete historical validation means you can act fast with full confidence.
What becomes possible:
Real-time backtesting: Test trading strategies against years of tick data in milliseconds, enabling mid-session algorithmic adjustments based on emerging market conditions.
Accelerated regulatory responseInstant payment validation: Process wire transfers and card authorisations with immediate access to complete customer transaction history and risk indicators.
High-frequency trading optimisation: Execute trades with real-time risk calculations informed by complete exposure data.
T+0 settlement: Support instant settlement requirements with immediate access to complete transaction chains and counterparty positions.
Talk to us about your data stackWhen a large wire transfer initiates, you can instantly query years of customer transaction patterns, account conduct and risk signals to validate legitimacy without blocking the payment.
For trading desks, live market data streams compare continuously against historical volatility patterns and correlation models, all queryable in milliseconds. Every execution decision is informed by complete market context: price movements, volume patterns, and correlation shifts from months or years of history.
For trading desks, live market data streams compare continuously against historical volatility patterns and correlation models, all queryable in milliseconds. Every execution decision is informed by complete market context: price movements, volume patterns, and correlation shifts from months or years of history.
When your Chief Risk Officer needs intraday visibility into consolidated positions across trading desks, or regulators demand T+0 trade reporting, the required data sits fragmented across systems. Accessing historical ledgers means ETL lag. Is that exposure spike a genuine threat or normal market movement? You won’t know until tomorrow’s batch job completes.
Streambased gives you total visibility into risk exposure and regulatory obligations. Retain and query 100% of transaction logs, trade records and compliance data in cost-effective Iceberg storage, without the prohibitive cost of keeping everything in hot systems.
Streambased gives you total visibility into risk exposure and regulatory obligations. Retain and query 100% of transaction logs, trade records and compliance data in cost-effective Iceberg storage, without the prohibitive cost of keeping everything in hot systems.
Key business benefits:
Reduced storage costs formandatory retention (MiFID II, Dodd-Frank, BCBS 239 compliance).
Accelerated regulatory response times (T+0 reporting capability).
Lower compliance and legal risk through reproducible audit trails.
Real-time risk visibility for better capital allocation.
What becomes possible:
Reduced storage costs formandatory retention (MiFID II, Dodd-Frank, BCBS 239 compliance).
Accelerated regulatory response times (T+0 reporting capability).
Lower compliance and legal risk through reproducible audit trails.
Real-time risk visibility for better capital allocation.
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. Yourtrading floors, payment processors and risk engines run on speed and generatemassive data volumes. Market ticks, card authorizations and trade executionsstream 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.
This raises multiple challenges:
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.
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.
