Banks and other financial services firms face a slow economic recovery, pessimistic economic forecasts worldwide, demands to improve their balance sheets, and more. As a result, banks are cutting costs, restructuring, optimizing business lines, and exiting less profitable activities – all while under pressure to satisfy new compliance regulations designed to protect against another economic…
The GridGain Systems In-Memory Computing Blog
Almost any In-Memory Data Grid (IMDG) solution available can be used as-is without an underlying persistent storage layer. Based on my experience, there are different use cases and real production scenarios when the entire data set is fully located in an IMDG and it is not synced to disk at all.
However, in a variety of deployments, companies still prefer to keep data both in memory and…
Introduction
Nowadays many companies are basing their applications and solutions on microservices architecture. One of the main benefits of this approach is that it allows splitting a solution into a number of loosely coupled software components (microservices). These software components might have their own release and life cycles, and even development teams. Moreover, these…
Fraud has evolved from a disorganized criminal activity into a sophisticated multi-billion dollar business. Fraud committed within financial services is causing loss of revenue, institution’s reputation, shareholder’s confidence and customer loyalty. As the fraudulent schemes become more sophisticated, so should the ways of fighting them.
Detecting fraud requires complex data…
In-memory computing allows users to process terabytes of data in real-time and across many different applications and underlying databases. In-memory computing is gaining momentum in industries such as financial services, fintech, software/SaaS, telecommunications, ecommerce, online services, and retailers for its ability to transact and analyze large amounts of data in real-time.
As the…
About 2 months ago I joined GridGain Systems and was introduced to Apache Ignite. At my very first meetup last month in New York, someone challenged me on the subject of how Ignite could guarantee ACID level consistency if Ignite was a highly available distributed system. I was familiar with CAP Theorem from my work with Apache Cassandra, but for the first time in my otherwise data…
When it comes to querying and acting on data — including in Big Data/Fast Data environments — SQL still dominates. And no other database-agnostic in-memory solution handles SQL functionality like the Apache Ignite In-Memory Data Fabric.Join us at 10:00 AM PT on Wednesday, March 23rd as Nikita Ivanov, CTO of GridGain Systems and member of the Project Management Committee for Apache® Ignite™,…
I would like to give my ideas on why I believe we are on the cusp of a new chapter in storage technology — namely an ascendance of an open smart storage standard. This standard should define an open vendor-agnostic interface for integration between compute and data processing and traditional storage systems. Using these open APIs, developers should be able run SQL, MapReduce, K/V access, file…
In my previous post I have demonstrated benchmarks for atomic JCache (JSR 107) operations and optimistic transactions between Apache Ignite™ data grid and Hazelcast. In this blog I will focus on benchmarking the pessimistic transactions.
The difference between optimistic and pessimistic modes is in the lock acquisition. In pessimistic mode locks are acquired on first access, while in optimistic…
Recently I have been doing many benchmarks comparing the incubating Apache Ignite™ (incubating) project to other products. In this blog I will describe my experience in comparing Apache Ignite ™ (incubating) Data Grid vs Hazelcast Data Grid.
Yardstick Framework
I will be using Yardstick Framework for the benchmarks, specifically Yardstick-Docker extension. Yardstick is an open…