Materialize

Materialize is a SQL streaming database for building internal tools, interactive dashboards, and customer-facing experiences. The company was founded in 2019 and is based in New York, New York. Incremental approach, transformative results. You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI. Built on Mature Technology Built on Timely Dataflow and Differential Dataflow, open source frameworks created by cofounder Frank McSherry at Microsoft Research. View Timeline → $100 Million Raised Funded by firms with a track record of success in databases. View Investors → Trusted in Production Data and Engineering Teams at industry-leading companies run production workloads at scale in Materialize. This website uses cookies We use cookies to personalise content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners who may combine it with other information that you've provided to them or that they've collected from your use of their services. DenyAllow selectionAllow all NecessaryPreferencesStatisticsMarketing Show details ❓ When to use Materialize vs a Stream Processor?   Read the Guide. Docs Product Pricing Blog About Sign In TRY FREE The Cloud Database for Fast-Changing Data. We put a streaming engine in a database, so your team can build real-time data products without the cost, complexity, and development time of stream processing. GET A DEMO GET STARTED Trusted by data and engineering teams View Customer Stories. WHY MATERIALIZE? New Use Cases in OLAP need Correct, Up-To-Date Data. Fresh, correct and consistent data is a prequisite to operationalizing OLAP data in your business. Real-Time & User-Facing Analytics Dashboards and data products need to be reactive to up-to-the-minute changes in your business. Read More  → Automation and Alerting Save time for your users, and build value by taking action or notifying at only the right moments. Read More  → Segmentation and Personalization Value of personalization, recommendations, dynamic pricing increases as latency of data aggregations approaches zero. Read More  → ML in Production Online feature stores need continually updated data, operators need to monitor and react to changes in ML effectiveness. Read More  → Traditional tools don't meet the requirements. Cloud Warehouses are easy, but they get expensive when run continuously, and hit hard limits on latency and concurrency. Stream Processors are fast, but they're a low-level tool, using them has a high-engineering cost, and results in complex architectures. Materialize combines the best of both worlds. Materialize is a fast, distributed SQL database built on streaming internals. Distributed and Cloud Native Materialize separates storage and compute for a cloud-native architecture — and adds in a serving layer. Read: Materialize Architecture  → Streaming Engine Results Write Read Results are always up-to-date Work is done at the moment of data arrival, rather than query time, so that maintained results are available almost instantly. Read: What is a Streaming Database?  → Managed in standard SQL Give your team streaming data capabilities without changing their language or workflow. Incrementally Maintained Views Write complex SQL transformations as materialized views that efficiently update themselves as inputs change. LEARN MORE Sliding Windows SQL Alerting incremental.sql CREATE MATERIALIZED VIEW my_view AS SELECT userid, COUNT(api.id), COUNT(pageviews.id) FROM users JOIN pageviews on users.id = pageviews.userid JOIN api ON users.id = api.userId GROUP BY userid; $  userID api_calls pageviews VPLaKV 400 20 MN37Mt 60 9 1fT4KY 72 42 sT4QY 10 342 Presents as PostgreSQL Manage and query Materialize using any Postgres driver or tool. Streaming Inputs Pull in streams of data from Kafka or stream from Postgres via replication. Built for JOINs Multi-way, complex join support across real-time streams - all in standard SQL. Active Replication Use replication to increase availability, reduce downtime, scale seamlessly. Low-Latency Serving Layer Results can be maintained in memory, making read latency similar to Redis. Event-Driven Primitives Sink changes out to Kafka, or subscribe to query updates in standard Postgres. Secure and Compliant SOC 2 Type 2 compliant, encrypted at rest, secure connectivity to your infra. READ THE DOCS Empower your Team to Build Confidently With Live Data. WORLD-CLASS TECHNOLOGY Streamline solutions to hard problems. Materialize is built from the ground up to deliver where others have fallen short: incremental view maintenance that doesn't sacrifice consistency and correctness, and without limits on SQL complexity. Consistency in Streaming  → INTEGRATED AND COMPATIBLE Keep the stack and workflows you love. Get new capabilities without disruptive changes: PostgreSQL Source Kafka Sources and Sinks dbt Adapter PG Wire Compatibility View All Integrations  → STANDARD SQL Replace bespoke architectures with SQL. Save your stream processor for the most complex use cases, everything else can be SQL queries. CLOUD ARCHITECTURE Make fresh data accessible across teams. Run multiple workloads on the same data with ease: Shared Storage: access the same raw data, updated continually. Isolation of Compute: develop without resource contention. Materialize Architecture Explained  → Trusted By Data Teams Emily Hawkins Data Infrastructure Lead, Drizly We can write real-time SQL, exactly the same way as we already are in Snowflake with batch. See how Drizly uses Materialize  → Ryan Gaus Staff Engineer and Tech Lead, Density Materialize has saved us I-don't-know-how-many untold quarters of trying to build our own thing. See how Density uses Materialize  → Jean-Francois Perreton Head of Algo Quant, Kepler Chevreaux Materialize directly integrates with our third-party applications, BI tools, you name it. It's really SQL. See how Kepler uses Materialize  → Tyler Richie Cofounder and CTO, Sproutfi With Materialize we don't have to worry about avoiding complex joins with streaming data; we can just do them very easily. See how Sproutfi uses Materialize  → Johan Stuyts Data Architect, Maqqie Materialize is correct, and not just eventually consistent. The alternatives simply don't support consistency, and you end up wasting a lot of time troubleshooting. See how Maqqie uses Materialize  → SHOW MORE... Try Materialize Free Get hands-on with Materialize in a 14-day Free Trial. Bring your own data, or use data sources we provide. TRY FREE GET A DEMO Footer OUR PRODUCT Customer Stories Sign In to Materialize Get Early Access Get a Demo of Materialize DEVELOPER Documentation Quick Start Key Concepts DATA GUIDES Streaming Databases Materialized Views Incremental Computation Streaming SQL Kafka is not a Database COMPANY Blog Events Careers News & Press Contact JOIN THE MATERIALIZE COMMUNITY Join hundreds of other Materialize users and connect directly with our engineers. JOIN THE COMMUNITY ©  2023  Materialize, Inc. Terms of Service | Privacy Policy