Materialize lets you ask questions about your data, and then get low-latency, correct answers, even as the underlying data changes.
Why not just use your database’s built-in functionality to perform these same computations? Because your database often acts as if it’s never been asked that question before, which means it can take a long time to come up with an answer, each time you pose the query.
Materialize instead keeps the results of the queries and incrementally updates them as new data comes in. So, rather than recalculating the answer each time it’s asked, Materialize continually updates the answer and gives you the answer’s current state from memory.
Importantly, Materialize supports incrementally updating a much broader set of views than is common in traditional databases (e.g. views over multi-way joins with complex aggregations), and can do incremental updates in the presence of arbitrary inserts, updates, and deletes in the input streams.
Many streaming solutions require the denormalization of data in order to maintain low latency - thereby prohibiting any kind of data enrichment like joins.
By contrast, Materialize offers joins in a SQL interface - including complicated multiple-way (e.g. 6-way, 12-way) joins across disparate data sources.
Materialize provides a single element in the most common programming language. Lower the burden on your data platform team and reuse skills from traditional SQL queries and applications.
We support the TPC-H benchmark - a standard built for industry-wide relevance, large data volumes, and high query complexity - with incremental updates.
Materialize connects directly to your streaming infrastructure, ingesting streams of data from event stream processors like Kafka. Or connect us directly to your database as a read replica.
Simplify your streaming data architecture. With Materialize, there is no pre-processing of data required to make the system work.
Want to run queries against both real-time and historical data? Simply connect Materialize to a storage system like Amazon S3 and union both sources together for a full picture of your data.
The potential for these kinds of enriched streams is massive, and we’re excited to see what our customers can do.
Materialize is wire compatible with PostgreSQL, presenting to downstream tools like any Postgres database, simplifying the development of custom applications and streamlining the process of connecting existing data analysis tools.
Even non-technical users can unlock the most complex real-time queries just using standard BI tooling.
Transformations on streaming data can be used to feed into other transformations of that data - say joining several sources, then further querying that information.
Incrementally updating feeds can be directed back into Kafka as a topic for additional applications.
Traditional read replica databases are optimized for transactional loads – but are usually suboptimal for analytics or visualization. As queries increase, the system can slow down considerably. Using a traditional data warehouse speeds up analytics queries – but on stale data.
Materialize can be used as a highly performant read replica of your relational database – enabling blazing fast analytics queries for data sets both large and small – while staying up-to-date within milliseconds of newly committed transactions.
Materialize can deploy in three ways - as a shared deployment in our multi-tenant cloud, within a customer-specific VPC cloud (managed and owned by Materialize), or within a customer’s public cloud account.
Our system offers high availability via active-active replication. Materialize delivers the output benchmarks of extreme scalability without the performance and expense trade-offs of traditional approaches to scaling.
Materialize offers all of the benefits of Timely Dataflow and Differential Dataflow as a managed cloud service. Both systems have been in open-source development for over 4 years and run in production deployments at global scale.
Timely Dataflow is a low-latency cyclical dataflow computational model that excels at delivering high performance, expressive computation, and consistent results.