Big Data Systems • Real-time Analytics (OLAP Engines)Medium⏱️ ~3 min
Real-Time OLAP vs Data Warehouse Trade-Offs
Real-time OLAP and traditional data warehouses solve fundamentally different problems with opposing trade-offs around freshness, query flexibility, and cost. Data warehouses like Snowflake or BigQuery excel at complex, ad hoc exploration with full American National Standards Institute (ANSI) Structured Query Language (SQL) support including heavy joins, subqueries, and window functions. They update on hourly or daily batch cycles and can handle unpredictable query patterns because they're optimized for throughput over latency. A typical warehouse query might take 5 to 30 seconds scanning terabytes with no SLA guarantees.
Real-time OLAP inverts these priorities. You get seconds-level freshness (events queryable within 10 seconds of creation) and predictable sub-second p95 latency, but you sacrifice query flexibility. Joins are minimized or materialized ahead of time because runtime joins destroy latency SLAs through network shuffles and poor locality. Schemas must be denormalized into wide fact tables during ingestion. Star schema and snowflake schema patterns get flattened upstream. Where a warehouse might join fact tables with dimension tables at query time, real-time OLAP precomputes those joins, storing enriched dimensions directly in the fact table.
The cost model differs dramatically. Warehouses are cost effective for batch workloads because they can use cheaper storage and compute, charging by query scan volume. Keeping large volumes "hot" for instant scans in real-time OLAP is expensive: SSD storage costs 10x to 20x more than object storage, and you need abundant Central Processing Unit (CPU) and memory to maintain low latency at high concurrency. Amazon's retail analytics shows this pattern: sub-second queries on last 7 days (hot tier on SSD), multi-second to tens of seconds for older ranges (warm tier on object storage), falling back to warehouse for multi-month ad hoc analysis.
Choose real-time OLAP when you need user facing SLAs (dashboards, product features, operational monitoring) with seconds-level freshness and can constrain query shapes through denormalization and pre-aggregation. Choose warehouses when freshness can be minutes to hours, queries are exploratory and constantly changing, and you need full SQL flexibility for complex analytics.
💡 Key Takeaways
•Data warehouses provide full ANSI SQL flexibility with complex joins and subqueries but update hourly or daily; real-time OLAP delivers seconds-level freshness with constrained query patterns through denormalization
•Real-time OLAP achieves sub-second p95 latency by avoiding runtime joins and using pre-aggregations; warehouses tolerate 5 to 30 second queries because they prioritize throughput and ad hoc exploration
•Storage costs differ by 10x to 20x: real-time OLAP keeps hot data on SSD for millisecond access while warehouses use cheap object storage, accepting multi-second scan times
•Amazon's pattern shows the hybrid approach: sub-second queries on last 7 days (real-time OLAP on SSD), multi-second for older data (warm object storage), falling back to warehouse for complex multi-month analysis
•Choose real-time OLAP for user facing dashboards, operational monitoring, and product features requiring predictable sub-second SLAs; choose warehouses for analyst exploration, complex multi-table joins, and unpredictable query patterns
•Schema evolution is harder in real-time OLAP because changes require reprocessing segments and updating pre-aggregations; warehouses handle schema changes gracefully with late binding at query time
📌 Examples
Airbnb experimentation: Uses real-time OLAP for experiment Key Performance Indicators (KPIs) with minute-level freshness (booking rate by variant in last hour) but falls back to warehouse for deep dives (multi-week cohort retention analysis with complex user journey joins)
Cloudflare analytics: Serves customer facing dashboards from real-time OLAP (request volume by status code in last 24 hours, sub-second queries scanning billions of rows) while using warehouse for internal security research requiring arbitrary time ranges and complex threat correlation joins
LinkedIn feed insights: Real-time OLAP powers "engagement on your posts in last 7 days" with sub-100 ms p95, precomputing metrics like unique viewers and click through rate; data science teams use warehouse for multi-month A/B test meta-analysis with full SQL and arbitrary segmentation