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.
Operating in today’s always-on, high-volume, high-speed, high-expectation world requires a different level of processing enablement. When microseconds can mean the difference between success and failure, Hazelcast in-memory caching solutions can deliver blinding speed with scalable and flexible data caching.
Fraud has grown to epidemic proportions as our digital lives become increasingly interconnected, dependent, and exploitable. In-memory solutions such as Hazelcast IMDG and Jet enable real-time fraud detection or fraud detection machine learning at speeds and scale that drive security enablement to a new level.
Embed stream processing into your enterprise applications with Hazelcast to immediately process and respond to data.
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The speed of in-memory solutions can drive your customer's experience to a new level of complete engagement through a fully-integrated perspective enabled by Machine Learning and powered by Artificial Intelligence.
AIOps is the use of machine learning and artificial intelligence to automate IT operations and reduce bottlenecks associated with technology needs.
Apache Cassandra is one of the most popular NoSQL databases available today and is often used when high speed is required. However, since Cassandra is disk-based, performance will be capped by I/O limitations. Integrating Hazelcast IMDG with Cassandra makes data available at much lower latencies (in milliseconds) than Cassandra can achieve independently. The combined solution maintains the high availability and horizontal scalability of Cassandra.
Hazelcast can be used to accelerate and scale your SaaS or custom internal applications by increasing throughput and reducing the latency of data accesses on disk-based databases. Add Hazelcast as an in-memory story between your application and your database to handle more users and higher load while improving response times. Updates that your applications make to data in Hazelcast will be passed through to the underlying database to ensure data synchronization.
Hazelcast provides a cache-as-a-service for scalable, reliable, and fast caching. Applications can use Hazelcast as side-cache to their database, or place the database in-line behind the caching service.
Organizations rely on database caching to predictably scale mission-critical applications by providing in-memory access to frequently used data. As customer data grows exponentially, organizations of all sizes are turning to in-memory solutions to scale applications to meet service level agreements, offload over-burdened shared data services, and provide availability guarantees.
Digital transformation touches every part of the modern business. In-memory technology is one of the core enablers in today's data-intensive, always-on world. If speed, scalability, and stability are critical to your business, Hazelcast is the answer.
ETL is an acronym for “extract, transform, load.” Extract refers to collecting data from some source. Transform refers to any processes performed on that data. Load refers to sending the processed data to a destination, such as a database. ETL is a data processing concept dating back to the 1970s, but it remains important today because it is one of the most dominant frameworks for providing people and applications with data. Engineering and product teams load and preprocess data from a variety of sources to a number of destinations with ETL techniques and software.
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