Use Cases›Fast Data Store

Fast Data Store

Hazelcast Platform is a real-time data platform with a fast data store and stream processing engine for high-speed storage, retrieval, and processing of streaming data from message queue systems like Apache Kafka and AWS Kinesis.

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Introduction

Hazelcast Platform is a unified real-time data platform that consists of two main components, a fast data store, and a stream processing engine. The fast data store provides extremely fast, distributed, in-memory object storage and retrieval capabilities combined with a distributed, real-time compute framework. The stream processing engine provides high-throughput, low-latency processing on real-time streaming data retrieved from modern message queue systems like Apache Kafka, Apache Pulsar, and AWS Kinesis.

Unlike a cache which only delivers raw data in an accelerated way, the Hazelcast Platform fast data store provides a complete set of high-speed caching capabilities plus application runtime capabilities to help you build modern applications for data-intensive environments. 

If you need a cache, Hazelcast has you covered, and you also get capabilities that you can add to your infrastructure as your needs grow without adding any complexity. The Hazelcast Platform fast data store looks like a caching technology but with many more capabilities under the covers, including:

  • High-speed storage, querying, and retrieval of many different data structures, including custom objects, that are managed in-memory or in a tiered model across in-memory and disks/SSDs
  • A built-in distributed compute framework to run applications/jobs that are automatically parallelized across the cluster to leverage data locality while optimizing resource utilization
  • Horizontal scalability due to its distributed, cloud-native architecture
  • Fault tolerance with built-in subsystems like intra-cluster replication to create redundancy to avoid single points of failure
  • Disaster recovery capabilities including provisions to minimize recovery point objective (RPO) and recovery time objective (RTO)
  • Security features such as role-based access controls and over-the-wire encryption
  • Built-in caching patterns and functions

Business Requirements

Why Use a Fast Data Store

You use a fast data store to address two main requirements:

  1. You need faster access to data (i.e., lower latency), typically to address responsiveness SLAs, customer expectations, and to scale more easily
  2. You need up-to-date, enriched, ready-to-consume data products

In most cases, the two requirements above go together, because you don’t want fast access to only raw data, and you don’t want slow access to curated data products.

Many technologies in the market today might help you address requirement #1 to a certain extent but addressing requirement #2 is a lot more complicated. You typically need to add or build separate, standalone technologies and applications that create more complexity in your architecture. Maintaining all the moving parts becomes a heavy burden, especially if one of the components encounters a problem.

A fast data store with a built-in distributed compute framework can continuously ingest, filter, transform, and aggregate data from multiple sources to ensure your data is always ready for use by applications and end users. This can be done in a single runtime, which simplifies your architecture so you can add more capabilities without writing so much low-level integration code.

Use Case

A fast data store can power many types of technical use cases, including:

  • Hot data layer / digital integration hub
  • High-speed reference data store, especially for stream processing pipelines
  • Feature store for machine learning inference
  • Large-scale computations, e.g., Monte Carlo Simulations
  • Data-as-a-service
  • Caching-as-a-service

Why Hazelcast

The Hazelcast Platform Fast Data Store provides many benefits to an enterprise architecture including:

  • More real-time capabilities when combined with the Hazelcast stream processing engine
  • Reduced complexity by eliminating redundant, domain-specific data stores
  • Higher throughput for real-time processing, especially with the Hazelcast stream processing engine
  • Lower latency access to data
  • Efficient and reliable data processing within the Hazelcast Platform cluster in a data-local, distributed compute framework

 If you’re looking for a cache, consider how much more you can get with Hazelcast Platform so you can go beyond short-term technical details and create higher-impact applications.

CachingHazelcast Fast Data Store

Sub-millisecond latency

Variety of data structures

Connectivity to a wide range of data sources

Multi-cloud deployments

Real-time data updates

Continuous data ingestion

Continuous data enrichment

Architectural simplification (combined storage and compute in a single runtime)

High-speed distributed compute for large-scale number crunching

Integration with stream processing

Enterprise features to support business continuity and security