Real-Time Data Warehouse

What is a real-time data warehouse?

Real-time analytics stretch the limits of single-node databases, traditional data warehouses and data lakes — maxing out everything from performance to query run times, data freshness, costs and scalability.

Real-time data warehouses are built for speed, with the ability to query massive amounts of data — even at petabyte scale — within milliseconds.

What you need in a real-time data warehouse

Low-latency streaming writes

Ability to stream writes to your database in real time, with sub-second and millisecond responses.

Upserts

Combination of update and insert operations, as well as using a unique key to prevent duplicate records and maintain data consistency.

Incremental deletes

Option to delete records in near real time — and sync deletes from your primary database to any analytical queries you’re running.

Comprehensive JSON support

Query, index and expand nested JSON structure, regardless of depth. And, schema flexibility to modify as needed after initial setup.

Separation of compute + storage

Better data durability, manageability, elasticity and cost advantages compared to traditional, on-premises analytical processing.

SingleStore as a real-time data warehouse

Architecture
Manage petabytes of data with a three-tier architecture comprised of memory, cache and unlimited storage.

Performance
Handle high-concurrency workloads, supporting your intelligent applications with up to hundreds of thousands of users.

Developer experience
Get up and running with a few clicks so you can quickly move to production.

Scale
Support your most complex workloads to power real-time analytics applications.

Modernizing its Teradata enterprise data warehouse to move from batch data updates to real-time streaming reports with speed and scale.
Read the case study >