The Streaming SQLMaterialized View Engine

Powered by Timely Dataflow

 

Real-time materialized views over your streaming data

Write complex SQL queries.

Add data from a message bus like Kafka.

Your SQL views stay incrementally updated within milliseconds.

At millions of updates per second.

 

Sign up to get early access!

 
Materialize

Materialize Features

 

Write SQL Queries. Stream your data.

No manually glueing streams. No writing imperative code.

Declare what you want in SQL. Materialize keeps it up to date. Within milliseconds.

ANSI Standard SQL. Including joins.

No "inspired by SQL" half-broken query languages. Materialize supports ANSI Standard SQL 92. Use a SQL shell or your favorite database adapter.

Correct answers

No page long caveats about eventual consistency and out-of-order execution. Materialize results are always consistent and always up-to-date. Pretend that we reran every query every message.

Distributed. Fault-tolerant. Replicated.

Materialize is built on top of Timely Dataflow, which is distributed. Materialize also adds active-active cluster replication for fault-tolerance, and autoscaling orchestration.

Scales up... and down.

Scale up: process millions of messages per second on a single-digit number of machines. Scale down: run Materialize on your laptop with a single binary on a single core. Either way, it's likely faster than the competition.

Powered by Timely Dataflow

Materialize is built on top of a powerful stream processing engine: Timely Dataflow. Inspired by the award-winning Naiad project, Timely Dataflow has been in open-source development for 4 years and runs in production deployments at scale.

 

Our Team

Arjun Narayan

Arjun Narayan / CEO

Arjun was an engineer on the SQL team at Cockroach Labs, where he worked on performance and benchmarking. He has a blog.

Frank McSherry

Frank McSherry / Chief Scientist

Frank was at Microsoft Research Silicon Valley where he led the Naiad project and co-invented Differential Privacy. He has a GitHub repository named blog.

Cuong Do

Cuong Do / Head of Engineering

Cuong was the third engineer at YouTube, the founding head of Dropbox's first remote office in New York City, and an engineering manager at Cockroach Labs.

Nikhil Benesch

Nikhil Benesch / Engineer

Nikhil specializes in distributed systems, SQL databases, and build systems. He was previously an engineer on the core team at Cockroach Labs, where he worked on the replication engine.

Jamie Brandon

Jamie Brandon / Engineer

Jamie was a researcher at Relational AI, and was the CTO at Eve. Jamie also has a blog like all the other cool kids.

 

Join us. We are hiring engineers.