Early customers are using Materialize for real-time data visualization, financial modeling and to provide advanced intelligence capabilities for various SaaS applications in martech, logistics, and enterprise resource planning.
FREMONT, CA: Materialize, the streaming SQL database company, launched the open beta for Materialize Cloud, which offers the first standard SQL interface for streaming data. Materialize Cloud makes it simple for any business to understand streaming data, answer complex questions and build intelligent applications without needing specialized skills.
Materialize Cloud now makes it easy for companies of any size to incorporate streaming data into their applications. Unlike competing products that require customers to learn new languages, Materialize Cloud is based on industry-standard SQL and is easy to set up, scale, and begin using "out of the box."
"The ability to use real-time data for insights and value will determine which companies lead their industries in the years ahead," said Arjun Narayan, co-founder, and CEO of Materialize. "For Materialize, the main motivation behind the product is streaming data. Our team has been studying this topic for decades and we are especially proficient at providing streaming services to our customers."
The Materialize team includes engineers who were early employees of Cockroach Labs, Dropbox, and YouTube. Frank McSherry, co-founder and chief scientist of Materialize, led the research behind Timely Dataflow and Differential Dataflow, which serve as the basis of Materialize.
Key features of Materialize Cloud include:
Managed database-as-a-service, making it easy to set up and operate a streaming database and automate administration tasks.
Managed cloud service inclusive of deployment, security, maintenance, and upgrades of Materialize instances.
Operational visibility and insight with support for endpoint integrations with monitoring tools and the ability to troubleshoot database and SQL queries.
Millisecond-level latency, a SQL-first architecture, and the ability to handle real-time complex JOINS.
Integration with various data sources, including event streaming platforms like Apache Kafka and Amazon Kinesis, CDC, historical sources like S3 and local files, data lakes, and Postgres databases.
Out-of-the-box support for dbt, extending its tremendous support among analysts from batch data to streaming data.
Enterprise-grade security, with all data secured in dedicated networks and machines, with support for encryption in transit and at rest via industry-leading security (TLS 1.2, EBS-encryption, and SOC2 certification).