Cutting-edge insurtech firm Superscript uses Materialize in an analytics stack to power real-time Machine Learning decision engines that help them close more conversions and better serve their customers.

Using Materialize gave Onward the ability to provide a real-time delivery tracking UI in two weeks of work with minimal ongoing maintenance.

With Materialize, Density’s internal tooling data is now updated as data is processed, in milliseconds, allowing the Customer Success team to have a consistently correct and up-to-date picture of sensory data powering their people-counting software.

Kepler Cheuvreux streamlined their architecture into just Materialize and Kafka, removing Postgres replicas. With timely data being vital to their business, they replaced batch jobs with streaming workloads. They also enabled real-time alerting and dashboarding in Metabase and Grafana, having Materialize serve as the underlying source of information.

Materialize was the solution that met their real-time requirements with the lowest overhead. One Maqqie developer manages Materialize on his own.

SproutFi cut their development time by 50% with the services built by Materialize while saving 10% of data management and migration time. Materialize also enabled SproutFi to eventually eliminate Cassandra from their infrastructure, simplifying their data stack. Overall, because Materialize helped SproutFi save on maintenance costs, it’s changed how SproutFi is developing their product. The SproutFi team feels empowered to economically and easily try new projects.

Materialize helped Unimarket easily build and maintain dashboards, enabling Unimarket to feel equipped to build new dashboards and enhance the customer experience. With Materialize, Unimarket dashboards update 2x faster and their OLAP code base has reduced by 60%.

With Materialize, Datalot removed load from their existing SQL database. From a cost-savings perspective, using standard SQL allowed more people at Datalot to participate in the development process without having to hire additional support. From a tactical perspective, Datalot can now take the same analytics that previously were embedded in reports, and use them to notify people the moment something becomes an issue, rather than spending time looking through a report or dashboard.

Once a Drizly customer abandons their cart, they are notified of their pending order in a 30-minute window, rather than a 24-hour window, which was previously the case. By easily transitioning from batch processing to stream processing, Drizly reduced cart abandonment, increased revenue, and provided customers with a more seamless experience.