OLTP vs OLAP vs Time-Series Databases
OLTP, OLAP, and time-series databases are complementary, not interchangeable. Each is optimized for a specific workload: transactions, multidimensional analytics, or high-volume time-ordered data.
How the Three Categories Differ
OLTP systems back user-facing transactions: order entry, payments, core banking. They use row-oriented, normalized schemas, strict ACID guarantees, and optimize for many small reads/writes on “current state” records.
OLAP engines and data warehouses batch-load data from upstream systems and optimize for complex scans and aggregations over large historical ranges. They favor columnar layouts and heavy joins, with minutes-to-hours latency between event and queryability.
Time-series databases treat time as the primary dimension. They ingest append-only events at very high rates, organize data by timestamp and series key, and answer aggregations and filters over recent and historical time ranges with low latency.
In capital markets, this often maps to: OLTP for orders and trades, OLAP for risk and regulatory reporting, and a time-series engine for tick data, order books, and real-time monitoring.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Where Time-Series Databases Fit
Time-series databases specialize in “what changed over time” rather than “what is the current state” or “what is the dimensional breakdown.” Architecturally, they combine:
- write-optimized, append-heavy storage and time-based partitioning
- columnar access for scans over metrics, prices, or sensor values
- built-in time operations (windowing, downsampling, retention)
This makes them well-suited for market data, observability metrics, and industrial telemetry, where you need second-or-subsecond analytics on fresh streams, not just overnight cubes.
For deeper context, compare with columnar vs row-oriented databases and real-time analytics databases.
Combining OLTP, OLAP, and Time-Series in One Architecture
Most serious deployments use all three:
- OLTP keeps authoritative business state.
- The time-series database serves operational analytics and monitoring on live streams.
- OLAP consolidates everything for strategic, cross-sectional analysis.
Choosing correctly is about query pattern and latency: if time-ordered events at scale are central to the problem, a time-series database should be the primary analytical store, with OLTP and OLAP as supporting systems.