Exclusive interview with Dale Kim, Senior Director of Technical Solutions, Hazelcast

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Exclusive interview with Dale Kim, Senior Director of Technical Solutions, Hazelcast


by Analytics Insight


May 24, 2021

In-memory computing refers to the storage of data in the main random-access memory (RAM) of dedicated servers rather than in complex relational databases that run on slow disk drives. In-memory computing enables business customers, such as retailers, banks, and utilities, to easily identify patterns, analyze large amounts of data at runtime, and perform operations. The current decline in memory prices in the market is a major factor in the growing success of in-memory computing. As a result, in-memory computing has become profitable in a wide range of applications.

Speaking to Analytics Insight, Dale Kim, Senior Director of Technical Solutions, Hazelcast, explains how Hazelcast’s in-memory computing platform meets the growing demand for improved application performance, speed and scalability .

Please inform us about the company, its specialization and the services your company offers.

Hazelcast is an open-source software company based in San Mateo, California that provides a cloud-native application platform that includes in-memory computing and stream processing capabilities. The platform is used to add real-time capabilities into existing infrastructures as well as accelerate business applications to meet strict SLAs and promote innovation through greater experimentation. The Commercial and Enterprise edition of our software is licensed per node, on a subscription basis, and includes features that simplify production deployments, such as business continuity, reduced planned downtime , scale and security.

With what mission and what objectives was the company created? In short, tell us about your journey since the creation of the company?

The founding of the company was driven by the need for businesses to leverage their data faster. One way to achieve this goal was to place subsets of data in RAM and let applications reduce the bottlenecks associated with accessing data on disk. Although this architectural pattern has long been used in the form of caching, enterprises are looking for larger in-memory data sets, and having sophisticated, distributed in-memory technology was a much better option than per-application caches. or by node. This was only part of the story, however, as companies were also looking for large-scale data processing that could distribute work across all nodes in a cluster. This was a big advantage for using Hazelcast over in-memory databases which were mostly good for simple caching use cases. Hazelcast allows IT teams to easily create applications that could be deployed on multiple nodes and work together in parallel while reducing network and disk access by reading data into memory that resides on the same node as each instance of ‘application.

The most recent innovation for Hazelcast has been to extend the computing framework to allow for the processing of data streams. Hazelcast can read a continuous incoming stream of data at high speed, applying a variety of operations such as transformations, filtering, aggregations, and machine learning scoring, with extremely high throughput and low latency. Stream processing capabilities work in conjunction with the in-memory framework to dramatically reduce latency and enable more computational work in less time.

Tell us how your company contributes to the country’s IoT/AI/Big Data Analytics/Robotics/Self-Driving Vehicles/Cloud Computing industry and how the company benefits customers.

The industries mentioned above both have two key characteristics: a massive amount of data and the need to process it quickly. Hazelcast addresses the challenges associated with these data characteristics with its inherent design principles. First, Hazelcast is designed to be lightweight and self-contained with no external dependencies to run, which greatly facilitates its integration into existing IT infrastructures. It also makes it easier to deploy in any environment, including IoT deployments at the edge away from a central data center, as most edge IT deployments tend to have limited physical space and therefore less material resources.

Second, Hazelcast includes many performance optimizations that take advantage of all available computing resources, which also makes it ideal for large-scale data processing environments. Since Hazelcast is cloud native and can scale elastically, a cluster can easily grow as data sets grow. Extreme performance, scalability and efficiency were recently showcased in a benchmark test where Hazelcast processed 1 billion data records per second on a data stream, with one millisecond latency on just 720 processors virtual in the cloud.

Third, Hazelcast places great importance on data security, both from a reliability and security perspective. Since many customers use Hazelcast for mission-critical deployments that run their operations, downtime would result in significant losses. Hazelcast’s high availability and disaster recovery capabilities ensure continuity even in the event of hardware or site-wide failures. With its built-in security capabilities, Hazelcast can also support environments containing sensitive data and prevent unauthorized access.

How are disruptive technologies such as IoT/Big Data Analytics/AI/Machine Learning/Cloud Computing impacting innovation today?

These disruptive technologies allow companies to become much more efficient and much smarter in how they shape their business strategies. Indeed, these technologies aim to overcome the limitations of manual effort and thereby achieve automation, real-time responsiveness and economies of scale. Interestingly, new levels of automation lead to increased investment in human resources to enable a continuous cumulative effect on business speed and efficiency. For example, the agility gained through the adoption of cloud computing allows companies to focus more on innovative efforts to create more business value, rather than allocating those human resources to infrastructure maintenance.

How does your company help customers achieve relevant business results through the adoption of business technology innovations?

Real-time responsiveness and greater opportunities for innovation are just two themes that enable customers to gain competitive advantage. A Hazelcast customer is able to aggregate data around its own customer interactions, and based on the full history of customer interactions, the company is able to immediately identify a set of products. By driving these recommendations based on the most recent interactions that are captured in their system as “event data,” they achieve the goal of offering the right product at the right time. This implementation on Hazelcast resulted in a significant increase in deal conversions, making the initiative profitable. Another client uses Hazelcast to identify fraud in financial transactions. Certainly, the more fraud they can prevent, the more they add to their bottom line. They hypothesized that if they created multiple machine learning-based fraud algorithms and ran them simultaneously to create a composite fraud rating score, they could get more accurate predictions about how fraudulent a transaction was. or the legitimacy of an otherwise suspicious transaction. . Thanks to their greater scoring accuracy made possible by the performance headroom Hazelcast gave them, they were able to save millions of dollars a year.

How does your company’s rich expertise help uncover patterns through powerful analytics and machine learning?

The biggest challenge today with advanced analytics, especially machine learning initiatives, is getting the machine learning models into production. Limited resources, sub-optimal technologies and a poor distribution of skills all contribute to this challenge. Companies need to simplify the process to achieve a higher success rate of deployments, and therefore greater opportunity for experimentation that can lead to greater business success. Hazelcast offers significant performance improvements over traditional machine learning deployments that require a lot more infrastructure and create a lot more complexity. The Performance Headroom gives customers the freedom to try new things and learn with a fast feedback loop. The relative simplicity of deploying machine learning models in Hazelcast is to allow IT teams to plug models directly from their data scientists, including in Python language, into the data pipeline without significant manual effort. Even the “Upgrade Tasks” feature in Hazelcast simplifies the effort by allowing teams to replace existing machine learning models with newer versions without downtime or data loss.

Mention some of the awards, achievements, recognitions, and customer feedback that you think are notable and valuable to the business.

In its early days, Hazelcast was selected as a Gartner Cool Vendor for Application and Integration Platforms for its innovation in in-memory technologies that enable customers to build applications that require rapid access to data. Most recently, Hazelcast was selected as one of North America’s Top 100 Private Technology Companies by Red Herring for its Global 2000 market adoption and innovative technology. Hazelcast customers also received awards for their Hazelcast deployments, including The Banker’s Innovation in Digital Banking 2020 awards, in which 6 of 15 winners partnered with Hazelcast for next-generation global payment infrastructures and other digital transformation.

What is the advantage of your company over other players in the sector?

A common problem with other technologies in the industry is that the complexity of their deployments stifles innovation. Since so many resources are spent on infrastructure, there is an opportunity cost to gaining a more competitive advantage. Hazelcast solves this problem with the simplified architecture designed to integrate well with existing systems. Additionally, Hazelcast benchmarks showed superior performance and scalability when it comes to data processing, which helps address a major concern for enterprises facing ever-increasing workloads.

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