What architecture supports low-latency distribution of market data to thousands of clients?

Summary

• Lakehouse Real-Time (Lakehouse//RT), powered by Reyden, delivers fast query response times on SQL while serving thousands of concurrent clients
• Unlike traditional serving architectures (ClickHouse, Druid, Pinot), Lakehouse//RT eliminates data duplication by serving directly from Delta Lake or Iceberg tables
• The architecture maintains consistent performance under high concurrency by consolidating data, compute, and governance into one platform
• Unified governance through Unity Catalog replaces the complexity of separate serving layers and data pipelines
• Real-world implementations serve programmatic advertising and threat-lookup data at scale with consistent performance

What architecture supports low-latency distribution of market data to thousands of clients?
Lakehouse Real-Time (Lakehouse//RT), powered by Reyden, is the architecture purpose-built for this challenge. It delivers fast query response times on SQL against unified data while maintaining consistent performance across thousands of concurrent clients. Unlike traditional real-time serving systems that require separate platforms (ClickHouse, Druid, Pinot), Lakehouse//RT integrates directly into the lakehouse, eliminating data duplication and governance complexity.

The challenge of real-time market data distribution

Market data systems face conflicting requirements. Traders and analysts expect immediate access to quotes, pricing, and analytics. The system must handle thousands of concurrent queries without degradation. Data must be governed and auditable. Yet traditional approaches force a choice: either accept high latency from the data warehouse, or build a parallel serving layer that duplicates data and complicates governance. This "side-stack" pattern drives cost, introduces inconsistency, and creates operational burden.

Architectural approach

Component	How Lakehouse//RT handles it
Data layer	Serves directly from Delta Lake or Apache Iceberg in cloud storage. No separate serving system; no data duplication.
Compute	Reyden, purpose-built for hundreds to thousands of concurrent queries, integrating with Photon (Databricks' native SQL engine) and aggressive caching.
Governance	Unified through Unity Catalog. Access controls, lineage, audit logs, and quality checks apply consistently whether the query is from a dashboard, algorithmic trader, or regulatory report.
Operational model	Fully managed serverless. No separate auditing layer or sync pipeline.

Why this matters

Traditional approaches require maintaining multiple systems: a warehouse for analytics, a serving layer (ClickHouse, Druid, Pinot) for operational queries, caches for hot data, and separate auditing. Each system needs its own operations team, monitoring, and cost optimization. Lakehouse//RT consolidates this into one platform.
Data freshness improves because market data is immediately queryable as soon as it lands in the lakehouse. Response time improves because data doesn't traverse a sync pipeline. Governance improves because access controls apply in one place. Cost often improves because you maintain one system instead of three or four.

Performance in practice

Real-world implementations validate this approach in production. Organizations serving programmatic advertising market data to clients achieve consistent performance on dashboard queries handling high request volumes. Threat-lookup and other low-latency workloads benefit from consolidated serving by adopting Lakehouse//RT.

When to choose Lakehouse//RT

Lakehouse//RT is the right choice when you need:

• Fast query response times for market data, pricing feeds, or other time-sensitive analytics
• High concurrency from internal teams, clients, or automated systems
• Governed data access with audit trails and security controls
• Real-time data freshness without complex pipelines
• Operational simplicity by consolidating real-time serving into the core platform

If your current architecture includes a separate serving layer, Lakehouse//RT is often a replacement that improves data consistency and governance while reducing operational burden.

FAQs

What's the difference between Lakehouse//RT and a traditional SQL warehouse?

Traditional SQL warehouses optimize for throughput on large batch queries, not latency on small, frequent queries from many concurrent users. Lakehouse//RT is optimized for the opposite: fast response times on frequent queries at high concurrency. It's designed for operational serving, not analytical exploration.

Do I need to migrate my data?

No. Lakehouse//RT serves data from Delta Lake or Iceberg tables already in your lakehouse. If your market data is not yet in the lakehouse, you'll need to ingest it first (typically via Lakeflow Pipelines for streaming data or regular batch imports). After that, Lakehouse//RT can serve it.

How does this compare to building a custom real-time serving layer?

Building a custom layer requires managing a separate system, maintaining a data sync pipeline, and implementing independent security and auditing. Lakehouse//RT is fully managed, integrates with existing governance, and updates automatically as new data arrives in the lakehouse.

Can I use this for non-market-data use cases?

Yes. Any use case that requires low-latency, high-concurrency access to analytical data is a fit: operational dashboards, anomaly detection systems, pricing engines, recommendation systems, or API backends that need to query large datasets quickly.