This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing? In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Real-time, push-based propagation of changes to a Hazelcast IMDG cache from a system of record.
Hazelcast IMDG Striim Hot Cache ensures continuous synchronization between the cache and its underlying database, providing consistency with the system of record.
Real-Time, Push-Based Propagation
Striim’s Change Data Capture (CDC) functionality captures changes in underlying databases in real-time, and pushes them instantly to the cache, keeping it hot.
Change Data Capture Functionality
Striim recognizes which tables and key-values have changed, immediately captures these changes with its table and key, and using the Hazelcast IMDG Striim writer, pushes those changes into the cache.
CDC Wizard Speeds Deployment
Striim can configure the capture of change data from a variety of databases including Oracle, MS SQL Server, MySQL and HPE NonStop, and propagate that data to your Hazelcast IMDG cache.
Architecture
Reduces latency of propagation from a backend database into the Hazelcast cache to milliseconds, providing the flexibility to run multiple applications off a single database and keeping Hazelcast cache refreshes up to date while adhering to latency SLAs.
Recognizes which tables and key-values have changed, immediately capturing these changes with their table and key, focusing on incremental changes that minimize disruption to your existing workflows.
Allows changes to be applied to the specific domain model in the Hazelcast IMDG, including inserts, updates and deletes.
Supports Oracle, MS SQL Server, MySQL, HPE NonStop
Reduce the latency of propagation of data from a backend database into the Hazelcast cache to milliseconds.
Resources
BNP Paribas S.A. is a French international banking group and one of the largest banks in the world by total assets.
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast as a Hibernate second level cache within Spring Boot
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast into a Quarkus application.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.
