Why Prediction Markets Need Real-Time Streaming Infrastructure (And How to Build One with SQL)

Prediction markets require real-time streaming infrastructure because they must continuously update odds, match orders, and settle positions across thousands of concurrent users — all within milliseconds. A streaming database like RisingWave lets you build this entire real-time data layer using standard SQL, without the operational complexity of traditional stream processing frameworks like Apache Flink.

What's driving the prediction market boom in 2026?

Prediction markets have exploded into the mainstream. Kalshi posted a record $2.9 billion in weekly notional volume in March 2026. Polymarket just signed a deal to become Major League Baseball's official prediction market exchange. The NHL has multi-year partnerships with both platforms. CNN, CNBC, and the Wall Street Journal are integrating prediction market data into their coverage.

Meanwhile, IBM completed its $11 billion acquisition of Confluent on March 17, 2026, explicitly to make "real-time data the engine of enterprise AI and agents." The message is clear: real-time streaming infrastructure is no longer optional — it's the foundation of the next generation of data-driven applications.

For engineers building prediction market platforms, the infrastructure challenge is significant. You need a system that can handle real-time price discovery, high-concurrency order processing, instant settlement, and continuous risk monitoring — all at once.

What are the core technical challenges of a prediction market?

Building a prediction market platform presents four engineering challenges that traditional databases cannot solve:

1. Real-time odds and price calculation. As trades stream in, odds must update continuously. Users expect to see live prices that reflect the current state of the order book — not prices from 5 seconds ago.

2. High-concurrency order matching. During major events (election nights, championship games, March Madness), trading volume can spike by 1000x within minutes. The system must match orders without race conditions or data loss.

3. Settlement fan-out. When a real-world event resolves — say, a team wins the Super Bowl — a single oracle message must instantly settle hundreds of thousands of open positions and update user balances atomically.

4. Continuous risk monitoring. Operators need real-time visibility into exposure, unusual trading patterns, and potential market manipulation. This requires streaming analytics running continuously alongside the trading engine.

Traditional OLTP databases handle transactions but cannot process continuous streams. Batch analytics systems provide insights but with minutes or hours of delay. What prediction markets need is a system that combines streaming computation with database-level consistency.

How does a streaming database solve these challenges?

A streaming database like RisingWave bridges the gap between stream processing and data serving. You define your business logic as SQL queries over streaming data, and the system continuously maintains the results as materialized views — always fresh, always queryable.

Here's how each prediction market challenge maps to a streaming SQL pattern:

Real-time odds with materialized views

Instead of recalculating odds on every API request, define a materialized view that RisingWave keeps automatically updated:

CREATE MATERIALIZED VIEW live_market_odds AS
SELECT
    market_id,
    outcome,
    COUNT(*) AS total_shares,
    SUM(amount) AS total_volume,
    SUM(amount) / SUM(SUM(amount)) OVER (PARTITION BY market_id) AS implied_probability,
    NOW() AS last_updated
FROM orders
WHERE status = 'filled'
GROUP BY market_id, outcome;

Every time a new order is filled, RisingWave incrementally updates this view. Your API serves the pre-computed results with single-digit millisecond latency — no recalculation needed.

Order enrichment with streaming joins

Enrich incoming orders with user risk profiles and market metadata in real time:

CREATE MATERIALIZED VIEW enriched_orders AS
SELECT
    o.order_id,
    o.market_id,
    o.user_id,
    o.amount,
    o.outcome,
    u.risk_tier,
    u.daily_volume,
    m.market_name,
    m.close_time,
    CASE WHEN o.amount > u.max_single_bet THEN 'FLAGGED' ELSE 'OK' END AS risk_status
FROM orders o
JOIN users u ON o.user_id = u.user_id
JOIN markets m ON o.market_id = m.market_id;

This streaming join runs continuously. The moment an order arrives, it's enriched with the latest user and market data — enabling instant risk checks without additional API calls.

Settlement with exactly-once semantics

When a market resolves, calculate payouts for all positions:

CREATE MATERIALIZED VIEW settlement_ledger AS
SELECT
    p.user_id,
    p.market_id,
    p.outcome AS user_bet,
    p.shares,
    p.cost_basis,
    r.winning_outcome,
    CASE
        WHEN p.outcome = r.winning_outcome
        THEN p.shares * r.payout_per_share - p.cost_basis
        ELSE -p.cost_basis
    END AS pnl
FROM positions p
JOIN market_resolutions r ON p.market_id = r.market_id;

RisingWave's exactly-once processing guarantees that each position is settled precisely once — no double payouts, no missed settlements — even if the system experiences a failure during the settlement process.

Real-time risk monitoring

Detect unusual trading patterns as they happen:

CREATE MATERIALIZED VIEW risk_alerts AS
SELECT
    user_id,
    market_id,
    COUNT(*) AS trade_count_1min,
    SUM(amount) AS volume_1min,
    MAX(amount) AS max_single_trade
FROM orders
WHERE created_at > NOW() - INTERVAL '1 minute'
GROUP BY user_id, market_id
HAVING COUNT(*) > 50 OR SUM(amount) > 100000;

This materialized view continuously identifies users placing an unusual number of trades or exceeding volume thresholds — enabling automated alerts and trading halts.

Why use SQL instead of a custom streaming framework?

The alternative to a streaming database is building a custom pipeline with Apache Kafka, Apache Flink (or its successor under IBM), and a separate serving database. This approach works but comes with significant operational cost:

For a prediction market startup moving fast, the difference between "we need a Flink expert" and "any backend engineer can write SQL" is the difference between shipping in weeks versus months.

How does this compare to traditional architectures?

A traditional prediction market architecture typically involves:

1. API layer receives orders
2. Message queue (Kafka/RabbitMQ) buffers events
3. Matching engine processes orders (custom code)
4. OLTP database stores state
5. Batch jobs compute analytics
6. Cache layer (Redis) serves real-time data

With a streaming database, you collapse steps 2-6 into a single system:

1. API layer receives orders
2. RisingWave ingests events, runs matching logic, maintains materialized views, and serves queries

The data flows in, SQL-defined transformations run continuously, and the results are always queryable. No separate cache to invalidate. No batch jobs to schedule. No Kafka consumers to manage.

Getting started

If you want to build a prediction market engine with RisingWave, check out our step-by-step tutorial: Building a Polymarket-Style Prediction Engine with RisingWave. It walks through the complete implementation, from market creation to settlement.

To try RisingWave locally:

curl https://risingwave.com/sh | sh

Or sign up for RisingWave Cloud with a 7-day free trial.

FAQ

Can RisingWave handle the throughput required for prediction markets?

Yes. RisingWave's distributed architecture scales horizontally to handle millions of events per second. In Nexmark benchmarks, RisingWave achieves up to 893,000 records per second on a single 8-core node, and outperforms Apache Flink in 22 out of 27 benchmark queries.

Is RisingWave suitable for financial-grade applications?

RisingWave provides exactly-once processing semantics and consistent snapshot reads, which are essential for financial applications where double-counting or missed transactions are unacceptable. However, RisingWave is a streaming database, not an OLTP database — it does not support read-write transactions.

How does RisingWave connect to existing trading infrastructure?

RisingWave natively integrates with Apache Kafka, Amazon Kinesis, Google Pub/Sub, Apache Pulsar, and 50+ other systems as both source and sink. It uses the PostgreSQL wire protocol, so any PostgreSQL-compatible client, ORM, or BI tool can query RisingWave directly.

Can I self-host RisingWave for a prediction market?

Yes. RisingWave is open source under the Apache 2.0 license. You can self-host it on Kubernetes or run it as a single binary for development. For production, RisingWave Cloud provides a fully managed service with dynamic scaling and enterprise support.

What makes RisingWave different from using Kafka + Flink?

RisingWave is a unified streaming database that combines ingestion, processing, and serving in a single system using SQL. Kafka + Flink requires managing multiple systems, writing Java code for stream processing, and maintaining a separate serving database. RisingWave achieves the same results with less infrastructure and only SQL.