Real-Time OLAP vs Alternatives: When to Choose What

The Core Question: In interviews, you will be asked why not just use an existing data warehouse or a streaming system with key-value stores. The answer lies in understanding what each system optimizes for and what trade-offs you are making. Compared to Traditional Data Warehouses: Warehouses are optimized for large batch queries that can take seconds to minutes. They excel at complex joins across massive historical datasets, heavy transformations, and queries that scan terabytes. If your business is fine with 6 to 24 hour delays for reporting dashboards, a standard warehouse is simpler and significantly cheaper. You avoid the complexity of maintaining dual pipelines, managing real-time segments, and the operational overhead of sub-second query serving. Real-time OLAP trades higher complexity and operational cost for much fresher data and lower interactive latency. The cost difference is concrete: at scale, real-time OLAP might require 3 to 5 times more infrastructure than batch warehousing for the same data volume because you are maintaining hot, indexed data and serving hundreds of queries per second with strict latency targets. Compared to Stream Processors Plus Key-Value Stores: A streaming job can maintain precomputed counters like clicks by region and hour in a key-value store with single digit millisecond read latency. This is perfect when metrics are known upfront and read patterns are predictable. However, every new dimension or metric requires code changes, testing, and deployment. If your product manager asks for clicks by device type and hour, you need to modify the streaming job and backfill historical data. Real-time OLAP provides flexible, ad hoc aggregations. Analysts can write new SQL queries without redeploying jobs. The trade-off is heavier storage and indexing overhead (storing raw or semi-aggregated data instead of just final counters) and more complex query execution. Query latency might be 200 to 500 milliseconds instead of 5 milliseconds, but you gain the ability to drill down into any dimension combination immediately. Design Choices Within Real-Time OLAP: Exact versus approximate aggregations represent another trade-off. Approximate algorithms like HyperLogLog for distinct counts can give p95 latency under 100 milliseconds and save 5 to 10 times CPU compared to exact computation, but results might be off by a few percent. For use cases like operational dashboards where exact precision is not critical, approximation is a win. For financial reporting or billing, exact results are mandatory despite higher compute cost. Dual pipeline (Lambda architecture) versus unified streaming (Kappa architecture) is a fundamental choice. Dual pipeline produces highly compressed, well optimized historical segments from batch processing, reducing query cost for older data. The risk is inconsistencies between the real-time and batch paths. If they compute metrics differently, numbers can jump overnight when real-time segments age out. Unified streaming simplifies correctness by processing all data through one path, but may be more resource intensive for maintaining long history. Decision Framework: Choose traditional warehouse when: freshness requirements are hours or days, query volume is low to moderate (under 100 QPS), and cost efficiency is paramount. Choose real-time OLAP when: freshness requirements are seconds to minutes, you need interactive query latency at high QPS, and business value justifies 3 to 5 times higher infrastructure cost. Choose stream processor plus key-value stores when: metrics are well defined upfront, read patterns are predictable, and you need single digit millisecond latency for specific counters.