Category: All posts
Apr 18, 2025

Posted by

Anya Sage
If real-time analytics performance is critical for the demanding application you’re building, this is the technical whitepaper you’ll be glad you read: Timescale Architecture for Real-Time Analytics.
In it, you’ll find out how to achieve the real-time analytics performance you’re seeking—without the headaches (because the database design does the heavy lifting for you) and without the learning curve (because it’s all on Postgres).
Interested but only got five minutes to spare? Here’s the whitepaper’s TL;DR.
Part 1 lays the foundation, explaining how real-time analytics presents its own challenges:
This section then outlines the six key requirements of real-time analytics systems (and explains how Timescale fulfills them):
With Timescale, developers can build low-latency, high-concurrency applications that seamlessly handle streaming data, historical queries, and real-time analytics while leveraging the familiarity and power of PostgreSQL. - Part 1
Part 2 dives into the four Timescale data model optimizations that enable real-time analytics in Postgres:
The result? High-performance queries and efficient compression (up to 95%) within Postgres, achieved with minimal developer effort.
Today’s applications demand a database that can handle real-time analytics and transactional queries without sacrificing speed, flexibility, or SQL compatibility (including joins between tables). Timescale achieves this... - Part 2
Part 3 outlines the three query optimizations that ensure queries are executed efficiently, reducing unnecessary work and maximizing performance.
Timescale optimizes every step of the query lifecycle to ensure that queries scan only what’s necessary, make use of data locality, and execute in parallel for sub-second response times over large datasets. - Part 3
Part 4 shows how continuous aggregates and hyperfunctions accelerate queries and how combining them allows developers to avoid tradeoffs they would otherwise have to make.
By combining hyperfunctions with continuous aggregates, Timescale enables real-time systems to deliver fast, resource efficient insights across high-ingest, high-resolution datasets—without sacrificing accuracy or flexibility. - Part 4
Part 5 covers features that enable Timescale Cloud to handle high-ingest rates and low-latency queries without overprovisioning:
Real-time analytics requires a scalable, high-performance, and cost-efficient database that can handle high-ingest rates and low-latency queries without overprovisioning. Timescale Cloud is designed for elasticity, enabling independent scaling of storage and compute, workload isolation, and intelligent data tiering. - Part 5
Part 6 lists the Timescale capabilities that provide the high availability, efficient resource utilization, and data durability needed for real-time applications:
These capabilities work together to keep Timescale performant even under demanding real-time workloads, and for applications that require both high availability and scalable performance.
Timescale provides robust operational features to ensure seamless performance under varying workloads. - Part 6
As you can see, Timescale has been architected for real-time analytics speed, scale, and savings. The proof is in production, as mentioned here. For the full deep dive complete with technical diagrams, read the whitepaper. And once you’re ready to test Timescale Cloud for your use case, you can access the 30-day free trial here.
Comparing databases for real-time analytics performance? Learn more by reading Not All Analytics Are Equal: Benchmarking Databases for Real-Time Analytics Applications.