Fast Cloud Applications
Large-scale computations need integration of transactional, operational, and historical data into a single platform. Connecting all the relevant data sources is hard enough, but procuring the compute power and storage space adds another level of complexity. You need these compute-intensive jobs to always complete within stringent SLAs, so you need high speed and efficient use of hardware resources. And you only want the resources you need, only when you need them.
Hazelcast provides the foundation to develop and deploy fast, highly scalable applications for you to run large-scale calculations, simulations, and other data- and compute-intensive workloads.
- Rapidly develop and deploy distributed applications using familiar programming patterns in SDKs for Java, C#, Go, Node.js, Python and C++. Leverage Hazelcast user-defined functions to reuse existing code into a modern, cloud-native architecture.
- Run zero-downtime jobs to ensure completion within SLAs, via elastic clustering, high-availability and multi-DC, dynamic patching, and app redeployment.
- Gain extremely high performance in a cloud-native architecture running as self-managed (bare metal, Kubernetes, Red Hat OpenShift), as a managed service on AWS, Azure, GCP, and at the edge.
Real-time decision-making entails immediately analyzing a comprehensive set of information to take the best action, often without any human intervention. But overwhelming complexity arises from disparate data silos plus the many moving parts that need to be bolted together in a real-time system. You end up struggling with low agility, putting your operations at a disadvantage. And you know it does not help to simply add more of the same components to your infrastructure.
Hazelcast enables optimal real-time decisions thanks to a low-latency, distributed memory architecture for data and computations.
- Integrate live data streams of any scale including remote applications, devices, and message brokers such as Apache Kafka, Apache Pulsar, AWS Kinesis, and RabbitMQ, with historical and operational data to add greater context for decisions.
- Efficiently run thousands of queries over billions of live events.
- Store indexed data in-memory as a fast, drill-down analytics store.
Accelerated Transaction Processing
Online transactions are often constrained by bottlenecks while accessing databases and other repositories spread across your organization. Building optimized, transaction-based applications on these silos is difficult because of performance, auditing, security, risk, and data safety reasons. You are forced to make trade-offs that lead to an unacceptable user experience that negatively impacts your business.
Hazelcast accelerates your transactions by significantly reducing data access latency via hybrid storage of data sourced from your many disparate data stores.
- Quickly develop fast applications with the SQL API for standards-based data access on transactional (as well as operational, streaming, and analytical) workloads.
- Execute thousands of concurrent, low-latency, distributed tasks that leverage the speed of a high-performance architecture. Automate data loading, unloading, and integration with remote data sources including databases, Hadoop, Amazon S3, Azure Data Lake and many more.
- Ensure increased data safety using persistence on flash/SSD.
A Real-Time Reference Architecture for Data-Driven Businesses
This real-time architecture paper gives you ideas on what to look for when pursuing a real-time business and gives some guidance on the high-level components of a real-time architecture you need to deliver the most value from your fresh data.
Achieving Real-Time for Competitive Advantage – A Technology Perspective from Silicon Valley
In this webinar, we will discuss how businesses are using data to achieve real-time operations, seen from a Silicon Valley perspective
HazelVision 11: Generic Record
Hazelcast’s distributed compute features have traditionally required developers to upload class definitions to the cluster, making it difficult to evolve classes or define them in non-Java languages. Generic records provide a more flexible way to describe data that avoids these issues.