What is Real-Time OLAP Architecture?

The Core Problem: Traditional OLAP data warehouses refresh data in hourly or daily batches. This works for executive reporting, but breaks down when businesses need to react immediately. Consider fraud detection: if you detect a fraudulent pattern only after processing last night's batch, millions in damage may already be done. The same applies to dynamic pricing, where stale data means you are optimizing against yesterday's demand, or real-time ad campaign monitoring where marketers need to see which creative is performing right now, not 6 hours from now. What Makes This Different: Regular OLAP systems optimize for complex queries over historical data: scans, filters, joins, and aggregations across billions of rows, typically using columnar storage and read optimized formats. Streaming systems optimize for real-time processing but usually produce simple metrics into key-value stores. Real-time OLAP sits in the middle, solving two requirements that are usually in tension: OLAP style analytical queries plus real-time ingestion at high write throughput, often handling hundreds of thousands to millions of events per second. Three Core Responsibilities: First, ingestion captures and processes event streams continuously from a durable log, with minimal latency between event occurrence and query visibility. Second, storage and indexing organizes data in formats supporting fast aggregations, using columnar segments with indexes on high cardinality dimensions like user ID or product ID, time partitioning, and rollups. Third, query serving distributes analytical queries across many nodes, executing them in parallel to return results within interactive latencies while handling hundreds to thousands of queries per second. The Architectural Pattern: Modern systems often separate real-time segments covering the last few hours from offline or historical segments for older data, while exposing a single logical table to clients. When you query for the last 24 hours, the system seamlessly combines fresh data from real-time segments with historical data from optimized batch segments. This dual layer architecture is what makes real-time OLAP practical at scale.