What Are OLTP and OLAP? Core Characteristics and Workload Shapes

Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) are two fundamentally different data processing patterns optimized for distinct workload shapes. OLTP serves the request path of applications: small, short lived transactions that create or mutate operational state with strict correctness guarantees. A typical OLTP transaction reads or writes just a handful of rows, targets sub 100 ms end to end latency (often single digit milliseconds within the database), and scales by handling large numbers of concurrent users. Amazon's DynamoDB, for example, serves more than 10 trillion requests per day with peaks over 20 million requests per second, demonstrating the massive concurrency OLTP systems must handle.

OLAP serves analytics and decision support: large, read heavy queries over historical data that scan and aggregate millions to trillions of rows. OLAP favors throughput over per row latency, often returning results in seconds to minutes. Where OLTP might read 10 rows in 5 ms, OLAP might scan 10 billion rows in 30 seconds. Meta's OLAP queries routinely scan tens to hundreds of terabytes, with interactive analytics completing in a few to tens of seconds on cached datasets.

The data modeling philosophies diverge sharply. OLTP uses highly normalized schemas to minimize write amplification and maintain integrity; every order, payment, or cart update must be immediately consistent and durable because it affects real world user visible state. OLAP denormalizes data into star or snowflake schemas, stores it column wise for compression and scan speed, and adds materialized aggregates. An OLTP order table might join to 8 normalized dimension tables; the corresponding OLAP fact table flattens those dimensions for fast aggregation without runtime joins.

The critical organizational principle is workload isolation: OLAP must not interfere with OLTP. Running a full table scan for analytics directly on your production transactional database risks lock contention, memory exhaustion, and latency spikes that degrade user facing transactions. This is why most companies at scale physically separate these workloads and use change data capture (CDC) or event streams to move data from OLTP into analytical storage.

💡 Key Takeaways

•OLTP optimizes for low latency point operations (sub 100 ms, often single digit milliseconds) with high concurrency, while OLAP optimizes for high throughput scans (seconds to minutes) over massive datasets

•OLTP transactions typically touch 1 to 100 rows with strict ACID guarantees; OLAP queries aggregate millions to trillions of rows with relaxed consistency during ingestion

•Data modeling diverges: OLTP uses normalized schemas to reduce write amplification, OLAP denormalizes into star/snowflake schemas to avoid expensive runtime joins and enable columnar compression

•Workload isolation is critical: running analytics directly on OLTP databases causes lock contention and latency spikes that degrade production transactions, which is why companies separate them physically

•Real scale examples: DynamoDB serves 10+ trillion requests/day at 20M+ peak QPS (OLTP), while Meta scans tens to hundreds of terabytes per query (OLAP)

📌 Examples

Amazon retail: Cart updates and order placement use OLTP (single digit ms, strict consistency), while sales analytics and forecasting use OLAP (scan billions of order rows in seconds)

Google Ads: Ad serving and billing use globally distributed OLTP with external consistency (single digit ms regional, tens of ms cross region), while auction analytics run on BigQuery OLAP (scan terabytes, return in seconds)

Uber: Trip state transitions target p99 under 50 ms with OLTP, while supply demand heatmaps and ETA quality analytics use streaming OLAP with sub minute freshness

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