Real-Time Analytics

Real-Time Analytics Database

Run analytics on live data streams with sub-second freshness. RisingWave continuously computes aggregations, joins, and window functions as events arrive — delivering real-time dashboards and insights without batch delays.

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Data Freshness

From event ingestion to queryable analytics result in under 100 milliseconds.

SQL

Full Analytics SQL

Window functions, aggregations, joins, and CTEs — all maintained incrementally on streaming data.

BI Ready

PostgreSQL Protocol

Connect Grafana, Superset, Metabase, or Tableau directly via standard PostgreSQL drivers.

Always-On

Continuous Compute

No batch scheduling or refresh cycles. Analytics update continuously as events arrive.

Why Real-Time

Why do traditional analytics databases fail at real-time use cases?

Traditional analytics databases rely on batch ingestion cycles — data arrives in micro-batches or scheduled loads, creating minutes-to-hours of staleness. They are optimized for scan-heavy ad-hoc queries, not continuous computation on live streams, making them unsuitable for dashboards and alerts that demand second-level freshness.

Factor	Batch Analytics	Real-Time Analytics (RisingWave)
Data Freshness	Minutes to hours	Sub-second
Ingestion Model	Scheduled batch loads	Continuous streaming
Query Execution	On-demand scan	Pre-computed materialized views
Dashboard Latency	Seconds per query	Millisecond point lookups
Cost Model	Pay per query scan	Pay per event processed
Ideal Workload	Historical reports	Live operational dashboards

How It Works

How does RisingWave enable real-time analytics with SQL?

RisingWave continuously ingests event streams and maintains materialized views that reflect the latest aggregations, joins, and transformations. BI tools query these pre-computed views for instant results, eliminating the scan-heavy queries that slow down traditional analytics databases.

Continuous Aggregations

COUNT, SUM, AVG, percentiles, and custom aggregations update incrementally as events arrive.

Streaming Joins

Join live streams with dimension tables, CDC feeds, or other streams using standard SQL join syntax.

Window Functions

Tumbling, hopping, and session windows for time-based analytics. Plus ROW_NUMBER, RANK, and LAG.

BI Tool Integration

Grafana, Superset, Metabase, and Tableau connect via PostgreSQL protocol for live dashboards.

Use Cases

What real-time analytics use cases does RisingWave support?

RisingWave powers real-time analytics across industries — from operational monitoring and fraud detection in fintech to live engagement metrics in adtech and IoT sensor analytics in manufacturing. Any use case requiring fresh, aggregated data benefits from RisingWave.

→Operational monitoring — track system health, error rates, and throughput metrics with sub-second dashboards
→Fraud detection — aggregate transaction patterns across sliding windows to detect suspicious activity in real time
→Live business metrics — revenue, conversion rates, and customer engagement that update continuously
→IoT sensor analytics — process millions of sensor readings per second with time-windowed aggregations
→User engagement tracking — real-time session analytics, funnel analysis, and A/B test metrics

Frequently Asked Questions

Can RisingWave replace my data warehouse for real-time queries?

How fresh is the data in RisingWave analytics?

Does RisingWave support window functions and aggregations?

Can I connect Grafana or Superset to RisingWave?

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Incremental Materialized Views →What Is a Streaming Database? →