Solutions/Real-Time Analytics

Real-Time
Analytics
on VeloDB

Ship interactive dashboards and data products that respond in milliseconds, on petabytes of data, at any concurrency.

Deliver sub-second dashboards to thousands of concurrent users with zero lag

Act on data that's seconds fresh, not hours stale. Spot anomalies before they become losses

Query your highest-cardinality datasets with standard SQL. No pre-aggregation, no workarounds

Go live in days: ingest directly from transaction databases and streaming sources with ecosystem support

Plug into your existing BI stack (Grafana, Tableau, Superset) with zero migration

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Real-time isn't a feature
It's how you win

Faster and better decisions made the moment the problem appears, not hours after it does

Act on what is happening, not what already happened

When inventory shifts, when a campaign spikes, when a fraud signal hits, your team sees it within seconds. The window between event and action collapses from hours to a heartbeat.

Sub-second on joins and updates, not just scans

Most real-time databases benchmark on static, single-table scans. Production hits multi-table joins on data that's changing by the second. VeloDB stays sub-second where others force you to pre-aggregate or denormalize first.

Customer-facing analytics that stay snappy at any scale

Whether 100 analysts or 100,000 concurrent users hit the same dashboard, latency stays flat. Embedded analytics, customer portals, and live APIs feel instant because they are.

Works with the tools your team already opens every morning

Tableau, Grafana, Superset, dbt, and Power BI all see VeloDB as MySQL. No new query language, no specialty drivers, no migration project. The team that built your stack keeps building.

Trusted in production

Industry leaders run on VeloDB

Kwai unified trillion-scale ad analytics on a single engine, replacing ClickHouse and Elasticsearch.

90%

Latency reduction

3M/s

Rows ingested per node

trillions

Rows in single tables

“We dropped slow query rate from 35% to under 5% and reduced point query latency from 250ms to 12ms. Real-time campaign attribution across 4,000 query templates and 700 fields now runs on one engine instead of three.”

Engineering Team, Kwai

400M+ daily active users

Read the full story

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Real-world tradeoffs

Challenges with real-time
analytics at scale

01·Freshness

Fresh data requires end-to-end real-time performance

Transactional databases can capture updates quickly, but they are not built for heavy analytical scans.

Warehouses can analyze large datasets quickly, but the data often arrives through delayed pipelines. Teams end up managing replication, sync delays, duplicate systems, and consistency gaps.

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How VeloDB solves it

Fresh data with fast ingestion and low latency queries

VeloDB lets fresh data be ingested and analyzed in the same database. Native columnar storage keeps analytical reads fast, Merge-on-Write compacts data during ingestion, and in-flight indexes make newly written data efficient to query. The result is fresh, consistent analytics without waiting for data to move through another system.

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02·Concurrency

High throughput is not high concurrency

OLAP databases were designed to push a single large query through as fast as possible, not to serve thousands of small, customer-facing queries in parallel.

Throughput-oriented designs collapse under concurrent load.

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How VeloDB solves it

Concurrency engineered for customer-facing applications

VeloDB handles high-concurrency workloads through efficient CPU scheduling, a rich set of indexes for aggressive data pruning, and partition and bucketing strategies aligned to access patterns so frequently queried data stays colocated. The result is predictable low-latency response across thousands of concurrent queries.

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03·Updates

Frequent updates slowing down queries

When data changes often, columnar databases have to rewrite large amounts of data or merge old and new versions at query time.

Either way, queries get slower as updates pile up.

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How VeloDB solves it

Updates that don't slow down your queries

VeloDB does the hard update work when data is written, not when users run queries. Each record is stored in its latest clean version, so queries can read fresh data directly without checking old versions or merging changes on the fly. This keeps analytics fast even when the underlying data changes frequently.

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04·Joins

Denormalizing data is often necessary for real-time analytics

Real-time databases are optimized for fast point and scan reads, not for joins.

Teams pre-flatten tables to compensate, multiplying pipelines, bloating storage, and creating stale copies that drift from source.

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How VeloDB solves it

Fast joins without needing to pre-flatten your data

VeloDB's cost-based optimizer uses statistics-driven join reordering to scan up to 10x fewer rows. Broadcast, Shuffle, Bucket Shuffle, and Colocate strategies are selected automatically based on data layout and query shape with no denormalization required.

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Architecture overview

VeloDB for real-time analytics

Ingest fresh events and CDC changes, keep mutable records current, transform data with materialized views, and run live SQL joins from one unified engine.

VeloDB real-time analytics engine

Data Sources

OLTP Databases

Postgres · MySQL

Event Streams

Kafka · CDC

Lakehouse Files

Iceberg · S3

Ingestion

Routine Load

Continuous from Kafka

Stream Load