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Introduction to In-Memory Data Grids


The speed at which today’s evolving data is exploding (90% of all data was collected in the last 2 years) imposes complex business problems that prevailing technology platforms can not address. This is preventing Enterprises from quickly extracting business value from this data. This poses further challenges as the value of data and the insights we can get from them decrease if it takes too long to take action.

In This Webinar

In this talk, we will learn how Hazelcast® addresses these problems and helps Enterprises overcome the challenges of extracting business value from massive scale data.

You will be introduced to distributed systems and in-memory computing with Hazelcast. This talk will cover some familiar distributed data structures like Maps, Lists, Queues, etc., along with running complex business algorithms in parallel over a Hazelcast cluster by using Distributed Executor Service, EntryProcessors and In-Memory MapReduce.

  • Seamlessly share Terabytes of in-memory data between many applications.
  • Create multi-application processes that operate faster and more fluidly than with traditional databases.
  • Easily share computational execution among applications and scale in runtime to hundreds of cores to achieve massive scale proportions.

This talk will enable you to get started exploring Hazelcast and give your project a jump start in application speed and scale.

Presented By:

Rahul Gupta
Rahul Gupta
Senior Solutions Architect

Rahul is a Senior Solutions Architect at Hazelcast® with years of experience in building and architecting scalable, low latency and high throughput infrastructure. His expertise lies in addressing challenges in Big Data and Real Time Analytics space. He specialises in In-Memory Data Grid (IMDG) and governing technologies and Enterprise Architecture. Rahul understands cloud space very well and has delivered several business critical applications deployed on highly mobile yet robust distributed clusters on on-premise, cloud based and/or virtual infrastructure.