The Cloud Operational Data Store
Use SQL to transform, deliver, and act on fast-changing data.
Trusted by teams to deliver fresh, correct results
A new way to deliver and query fresh data
Get strongly consistent views and join fast-changing data, without the hassle and within your budget.
Stop overloading your
operational databases
Hit your availability and performance SLAs by offloading demanding, query-intensive workloads from your mission-critical systems.
Eliminate read replicas and
100x your query performance
Capture database updates and create views that are kept incrementally up to date. Get much faster read performance using a fraction of the hardware.
Join your data in real-time using SQL
Break down silos. Use SQL to join data across databases and other sources to create strongly consistent and always up-to-date views on fast-moving data.
Reduce your data warehouse spend
Avoid sending real-time, query-intensive workloads to your data warehouse just to watch costs soar. Move those queries to Materialize to keep them up to date at a fraction of the cost.
For use cases that get better with fresher data and more data sources.
Real-Time Process Optimization
Get fresh, consistent views of your business across data sources to quickly and confidently react to changes.
Fraud Detection
Improve profitability and customer satisfaction by flagging and mitigating malicious activity as soon as it occurs.
Anomaly Detection
Stream data from multiple sources in real-time and use SQL to identify patterns that deviate from normal behavior.
Automation and Alerting
Remove delays and move faster with real-time alerts and automated actions on fresh, trustworthy data.
Dynamic Customer Experiences
Use real-time customer signals to optimize a customer’s experience while they are still engaging with your brand.
Generative AI
Deliver up-to-date context for retrieval augmented generation (RAG) models and LLM-powered agents.
By moving SQL models for fraud detection from an analytics warehouse to Materialize, Ramp cut lag from hours to seconds, stopped 60% more fraud and reduced the infra costs by 10x.
Ryan Delgado Staff Software Engineer, Data Platform - Ramp
Powered by a revolutionary engine
Built on Differential Dataflow, Materialize incrementally maintains results with sub-second latency
Work to maintain views is done incrementally and efficiently on inserts, updates, and deletes. Execute low-latency queries on the results.
Join fast-changing data across sources. All data throughout Materialize moves from one consistent state to the next as inputs change.
Accessible via a familiar, Postgres-compatible SQL interface with support for complex joins, aggregations, and even recursion.
Patterns enabled by Materialize
Query Offload
Scale complex read queries more efficiently than a read replica, and without the invalidation headaches of a cache. Simply send updates to Materialize, create materialized views using SQL, and directly query the correct, incrementally maintained results.
Operational Data Store
Materialize lets you extract, load, and incrementally transform data from multiple sources. Create live views of your data that can be queried directly or loaded into systems like data warehouses or stream processing platforms.
Operational Data Mesh
Use SQL to create and deliver real-time, strongly consistent data products to streamline microservice communication and coordination across domains.
Seamlessly integrate with your existing data stack
Power services and business processes with fresh data while keeping your data architecture intact
Materialize accepts updates from various sources, including OLTP systems, Kafka, and webhooks.
Pull results from Materialize using Postgres-compatible SQL, which can be issued from a service, a native web-client, or even a standard BI tool. Push updates to downstream systems like a data warehouse or Kafka.