materialize

Materialize is a real-time data integration platform that creates and continually updates consistent views of transactional data from across your organization. Its SQL interface democratizes the ability to serve and access live data. Materialize can be deployed anywhere your infrastructure runs. Use Materialize to do things like deliver fresh context for AI/RAG pipelines, power operational dashboards, and create more dynamic customer experiences without building time-consuming custom data pipelines. The three most common patterns for adopting Materialize are the following: Query Offload (CQRS) - Scale complex read queries more efficiently than a read replica, and without the headaches of cache invalidation. Integration Hub (ODS) - Extract, load, and incrementally transform data from multiple sources. Create live views of your data that can be queried directly or pushed downstream. Operational Data Mesh (ODM) - Use SQL to create and deliver real-time, strongly consistent data products to streamline coordination across services and domains. Get started Ready to try out Materialize? You can sign up for a free cloud trial or download our community edition, which is free forever for deployments using less than 24 GiB of memory and 48 GiB of disk! Have questions? We'd love to hear from you: Join our Slack Send us mail About Materialize focuses on providing correct and consistent answers with minimal latency, and does not ask you to accept either approximate answers or eventual consistency. This guarantee holds even when joining data from multiple upstream systems. Whenever Materialize answers a query, that answer is the correct result on some specific (and recent) version of your data. Materialize does all of this by recasting your SQL queries as dataflows, which can react efficiently to changes in your data as they happen. Our fully managed service is cloud-native, featuring high availability through multi-active replication, horizontal scalability by seamlessly scaling dataflows across multiple machines, and near-infinite storage by leveraging cloud object storage (e.g., Amazon S3). You can self-manage Materialize using our Enterprise or Community editions. We support a large fraction of PostgreSQL features and are actively expanding support for more built-in PostgreSQL functions. Please file an issue if you have an idea for an improvement! Get data in Materialize can read data directly from a PostgreSQL or MySQL replication stream, from Kafka (and other Kafka API-compatible systems like Redpanda), or from SaaS applications via webhooks. Transform, manipulate, and read your data Once you've got the data in, define views and perform reads via the PostgreSQL protocol. Use your favorite SQL client, including the psql you probably already have on your system. Customers using Materialize in production tend to use dbt Core. Materialize supports a comprehensive variety of SQL features, all using the PostgreSQL dialect and protocol: Joins, joins, joins! Materialize supports multi-column join conditions, multi-way joins, self-joins, cross-joins, inner joins, outer joins, etc. Delta-joins avoid intermediate state blowup compared to systems that can only plan nested binary joins - tested on joins of up to 64 relations. Support for subqueries. Materialize's SQL optimizer performs subquery decorrelation out-of-the-box, avoiding the need to manually rewrite subqueries into joins. Materialize can incrementally maintain views in the presence of arbitrary inserts, updates, and deletes. No asterisks. All the aggregations: min, max, count, sum, stddev, etc. HAVING ORDER BY LIMIT DISTINCT JSON support in the PostgreSQL dialect including operators and functions like ->, ->>, @>, ?, jsonb_array_element, jsonb_each. Materialize automatically plans lateral joins for efficient jsonb_each support. Nest views on views on views! Multiple views that have overlapping subplans can share underlying indices for space and compute efficiency, so just declaratively define what you want, and we'll worry about how to efficiently maintain them. We’ve also extended our SQL support to enable recursion that supports incrementally updating tree and graph structures. Just show us what it can do! Here's an example join query that works fine in Materialize, TPC-H query 15: CREATE SOURCE tpch FROM LOAD GENERATOR TPCH (SCALE FACTOR 1) FOR ALL TABLES; -- Views define commonly reused subqueries. CREATE VIEW revenue (supplier_no, total_revenue) AS SELECT l_suppkey, SUM(l_extendedprice * (1 - l_discount)) FROM lineitem WHERE l_shipdate >= DATE '1996-01-01' AND l_shipdate < DATE '1996-01-01' + INTERVAL '3' month GROUP BY l_suppkey; -- The MATERIALIZED keyword is the trigger to begin -- eagerly, consistently, and incrementally maintaining -- results that are stored directly in durable storage. CREATE MATERIALIZED VIEW tpch_q15 AS SELECT s_suppkey, s_name, s_address, s_phone, total_revenue FROM supplier, revenue WHERE s_suppkey = supplier_no AND total_revenue = ( SELECT max(total_revenue) FROM revenue ) ORDER BY s_suppkey; -- Creating an index keeps results always up to date and in memory. -- In this example, the index will allow for fast point lookups of -- individual supply keys. CREATE INDEX tpch_q15_idx ON tpch_q15 (s_suppkey); Stream inserts, updates, and deletes on the underlying tables (lineitem and supplier), and Materialize keeps the materialized view incrementally updated. You can type SELECT * FROM tpch_q15 and expect to see the current results immediately! Get data out Pull based: Use any PostgreSQL-compatible driver in any language/environment to make SELECT queries against your views. Tell them they're talking to a PostgreSQL database, they don't ever need to know otherwise. This is particularly helpful for pointing services and BI tools directly at Materialize. Push base