Financial Services

VeloDB for financial services

Trading venues, payment platforms, and crypto businesses run on data that changes by the second: prices, positions, balances, and risk signals. VeloDB, the real-time analytics and search database from the creators of Apache Doris, serves banking, capital markets, fraud detection, and digital-asset workloads in one engine, built for the concurrency and update pressure financial data brings.

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Banking & payments: Power real-time customer-facing analytics, monitor payment infrastructure end to end, and run risk and log analysis across billions of events at high concurrency.

Capital markets: From quant backtests to real-time P&L, work across decades of market data and millions of trade events per second on one live view of positions and risk.

Fraud detection: Score transactions and trading behavior against aggregates computed on data ingested seconds ago, with the concurrency investigation workflows demand.

Digital assets & crypto: Run on-chain analysis and live trading analytics at exchange scale, sustained through the launch-day traffic spikes DeFi brings.

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Customer Stories

Web3 market analytics

AVE.ai

~1,000 QPS

P99 < 1s

at peak

50%+

lower cost

AVE.ai evaluated ClickHouse and Snowflake for real-time token analytics, then chose VeloDB. It now sustains close to 1,000 QPS with data landing in under 5 seconds.

Read the story

Digital identity & risk

Advance.AI

50%+ lower cost

faster queries

Near real-time

log writes

Advance.AI moved its log system from OpenSearch to VeloDB and unified log analysis with analytics on one engine.

Read the story

Sovereign wealth fund

ADIA

3 → 1 systems

Sub-second

on live data

1 live view

for all analysts

ADIA unified PostgreSQL, TimescaleDB, and a separate analytics tier into VeloDB for its fully automated trading operation.

Story publishing soon

Powering real-time data for 10,000+ teams

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The Gaps

Where financial data stacks break down

Real-time analytics buckles under market pressure

When markets move or tokens launch, three things happen at once: query concurrency spikes, every price and balance update rewrites hot rows, and analytics queries join tables that are changing mid-query. General-purpose OLAP engines slow down at the moment the business needs them most.

AVE.ai hit all three limits while evaluating ClickHouse and Snowflake for Web3 market analytics. Read their story

Mixed workloads mean stitched-together systems

Time-series data lands in one store, point lookups run on an OLTP database, and aggregations get their own analytics tier. Every seam adds data movement, drift between copies, and one more system to operate, and each team ends up with its own version of the truth.

ADIA, a sovereign wealth fund running systematic trading across all asset classes, ran three systems before consolidating. Story publishing soon

Search and analytics live in separate engines

Logs and text sit in a search engine while metrics sit in an OLAP store, so teams pay to store the data twice, maintain two query languages, and join across systems to answer one question.

Advance.AI ran OpenSearch for logs alongside its analytics stack, at 50% higher cost. Read their story

The Answer

One engine for every analytic shape financial data takes

Four ways one engine fits how financial data actually behaves.

Performance

Fast where financial data is hardest

Merge-on-write updates keep tables current under heavy writes, so balances, positions, and prices are queryable the moment they change. Point queries reach 30,000 QPS per node, complex joins run distributed across the cluster, and high-concurrency dashboards stay responsive through peaks. ASOF JOIN, available in Doris 4.1, pairs each trade with the latest quote for execution analytics and trade surveillance.

One System

Time-series, OLAP, and point lookups in one system

One engine handles streaming market data, aggregations over any window, and OLTP-style entity lookups, so analysts share a single up-to-the-second view instead of reconciling per-team stores. Native CDC ingests from PostgreSQL; Kafka delivers streaming events.

The next use case is a new table, not a new database.

Search + Analytics

Search and analytics together

Inverted indexes serve full-text queries next to SQL aggregations, so log search, entity lookup, and metric analysis run on one copy of the data. Doris 4.1 adds BM25 scoring and Elasticsearch-compatible search syntax, with billion-scale vector retrieval for AI workloads.

Deploy Anywhere

Runs where regulators expect it to

Deploy as managed cloud, BYOC in your own account, or on-premises for full data residency control. Storage-compute separation scales for market peaks without paying peak capacity all year. VeloDB also queries Iceberg and Paimon tables in place, so lake data joins the analysis without another pipeline.

Workloads by Domain

What financial teams run on VeloDB

Pick a workload. Every one runs on the same engine, on the same live data.

Real-time trading analytics

Live P&L and positions refreshed as trades execute
Strategy dashboards shared across a full analyst pool
Time-window aggregations over 1-minute, 5-minute, 1-hour, and 24-hour ranges
Trade-to-quote joins for execution analytics, powered by ASOF JOIN

ADIA streams L1 market data and trade execution records through Kafka into VeloDB, giving every analyst one consistent, up-to-the-second view of markets, positions, and strategy performance. Story publishing soon.

ADIA

Transaction fraud detection

Behavioral scoring against aggregates computed on fresh data
Anomaly detection for volume spikes and large trades as they happen
Entity-history lookups inside live decisioning flows

AVE.ai runs trading-behavior analytics, volume, large trades, and fraud detection, on the same engine that serves its market analytics. Read here

AVE.ai

On-chain analysis

High-throughput ingestion from major blockchains, with heavy updates as chain state changes
Token, wallet, and pool analytics under launch-day concurrency
Sub-5-second latency end to end, sustained at AVE.ai

A top crypto exchange serves 80M+ users on VeloDB at 5,000 peak QPS with P95 latency under 500ms. Story publishing soon.

Crypto exchange

FinTech operations and log analysis

Log search and analytics on one copy of the data
Near real-time log writes for time-critical business functions
Fewer systems and a simpler architecture to run

Advance.AI unified its log system on VeloDB and cut costs by more than 50%. Read here

Advance.AI

FAQ

Frequently asked questions

What is VeloDB?

VeloDB is a real-time analytics and search database from the creators of Apache Doris. One engine handles point lookups, complex joins, high-concurrency aggregations, and full-text and vector search on data that changes by the second.

How does VeloDB support real-time fraud detection?

Fraud scoring needs fresh aggregates and fast entity lookups at the same time. VeloDB ingests transaction streams continuously, keeps aggregates current under heavy updates, and serves point queries at up to 30,000 QPS per node, so decisioning flows read live data instead of a batch from the night before.

Can VeloDB handle on-chain data?

Yes. AVE.ai ingests roughly 5,000 records per second from major blockchains with end-to-end latency under 5 seconds, and serves close to 1,000 QPS with P99 under 1 second during peak trading.

What workloads can teams consolidate on VeloDB?

Time-series analysis, OLAP aggregations, OLTP-style point lookups, log search, and vector retrieval run in one engine. ADIA consolidated three systems into one; Advance.AI unified log analysis with analytics.

How does VeloDB deploy for regulated financial institutions?

Managed cloud, BYOC in your own cloud account, or on-premises, with storage-compute separation for elastic scaling and full control over data residency.

Get Started

One database for real-time analytics, search, and AI retrieval

Start with one workload, grow without adding systems.

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Managed cloud · BYOC · On-premises

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