Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
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.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away. Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
The industry's most efficient database integration tool for data grids.
Overview
Hazelcast Auto Database Integration is a highly efficient time-saving tool for companies working with databases. It streamlines the development of Hazelcast applications by generating a Java domain model representation (POJOs and more) of the database, allowing companies to be productive with Hazelcast in no time.
Best of all, it integrates with any RDBMS: Oracle, SQL Server, DB2, AS400, PostgreSQL, MySQL, MariaDB, and SQLite are supported. It also supports most standard database types, including BLOB and CLOB.
Features
Automatically generates a Java domain model for the given database with POJOs (Portable), SerializationFactories, ClassDefinitions, MapStores, MapLoaders, ClientConfiguration, Ingest, and Index.
Changes in the database schema do not entail manual configurations. Instead, automatic schema migration can be performed to quickly adapt the application to the new schema.
Utility methods for bootstrapping the IMDG from an existing database via a single call are generated. These methods support the parallel loading of tables.
The IMDG does not need to have the generated classes on its classpath. New Hazelcast nodes can be added to an existing IMDG with no additional configuration or prior knowledge of existing applications. In addition, new IMDGs can easily be set up using Hazelcast cloud instances, enabling hassle-free initial project configuration.
Updates to the grid can easily be propagated into the database using write-through, write-behind or IMDG-only operations. Client-side persistence is also offered with write-through or Hazelcast only updates.
As an alternative to the Hazelcast API, customers are given the option to express CRUD operations as standard Java Streams. Using familiar APIs can reduce the learning curve and lower the risk for mistakes.
Resources
Hazelcast offers a handy productivity tool from Speedment that automates database integration. It streamlines the development of Hazelcast applications by generating a Java domain model representation (POJOs and more) of the database, allowing companies to be productive with Hazelcast in no time.
This use case outlines how a logistics company has cut maintenance costs and drastically reduced the overhead of setting up new applications. Hence, time to market is shortened by streamlining the process of keeping the data model of the in-memory data grid in sync with the data sources.
Hazelcast IMDG® is the industry’s leading in-memory data grid (IMDG) solution. In an environment driven by digital transformation, continuous intelligence, and a whole new level of performance expectations, enterprises need digital speed, massive scale and actionable perspective in order to thrive in a multi-cloud technology ecosystem.
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.
