Product & Technology
Frost & Sullivan Report: In-Memory Computing Yields Real-Time Insights from Big Data
Perhaps the number one imperative in the business world today is to capture all data that is relevant to the organization, from all available sources, and put it to work to support business objectives. Companies need to answer questions in order to more effectively run their businesses. Businesses are increasingly finding those answers through analytics. Analytics is the science of examining raw data in order to discover meaningful patterns in the data and draw conclusions from it. Analytics also describes the software and methods used to understand data. Organizations generate and collect data to gain insights into the behavior of customers and competitors, and into their own financial and operational performance, then leverage those insights to make more accurate predictions and smarter decisions.
Ovum On the Radar Cloudify Review
The more organizations that use cloud computing in all its incarnations, from private to public clouds, and every option in between, the more they need tools that help them automate and manage these various environments. Many vendors claim to meet this requirement, but few can do so effectively. While many tools are written for either a specific cloud (Amazon Web Services, for example) or a specific capability, such as compute, Cloudify supports multiple clouds and capabilities including compute, storage, and networking. Its new design and capabilities make it a strong shortlist contender as both a cloud application automation framework and a cloud abstraction layer, especially for organizations interested in OpenStack. It supports a wide range of applications from cloud-native to legacy, and use cases including network function virtualization (NFV), an up-and-coming use case for OpenStack.
Robert Frances Group Analyst Report: Delivering Mission-Critical Applications to the Cloud at the Speed of Business
The pace at which information technology innovation and adoption has transformed virtually every aspect of business and industry is breathtaking. In less than a generation, the advent of the Internet and intelligent mobile computing devices along with secure access to Big Data and vastly improved analytics capabilities have tipped the computing paradigm on its head. For those organizations responsible for supporting their own mission-critical applications and quickly bringing them to market, whether hosted privately, publicly or via a hybrid approach, a new more innovative approach is necessary. This need for speed to business value while maintaining control of a stable, repeatable deployment environment is the essence of Cloudify.
Using In-Memory Data Grid to Bridge the Cloud
A major challenge in moving applications from on-premise datacenters to public clouds is the reluctance to store sensitive data on the cloud, for various reasons: perceived lack of control over the storage, security concerns or non-compliance issues when data is stored beyond the enterprise's boundaries, or the need to store the data on-premise for other internal applications to access. A possible approach in such a scenario is to use a hybrid architecture where the bulk of the customer-facing application is moved to the public cloud, allowing it to leverage all the cloud’s advantages (scalability, cost, redundancy, DR, etc.), while the data store continues to reside on-premise.
GigaSpaces XAP Elastic Caching Edition
GigaSpaces XAP Elastic Caching Edition delivers an in-memory data grid for fast data access, extreme performance, and scalability. XAP eliminates database bottlenecks and guarantees consistency, transactional security, reliability, and high availability of your data. XAP Elastic Caching is the only product designed to dynamically scale the data layer, responding to loads in real time, while significantly boosting the performance of mission-critical applications. Partnered with powerful event handling and comprehensive administration and monitoring capabilities, this provides enterprise-grade availability and reliability, guaranteeing virtually zero unanticipated downtime. This paper provides an overview of the features and functionality offered by XAP Elastic Caching, including topologies, data replication, monitoring & management, access & query support, database integration, and more.
Inside GigaSpaces XAP Technical Overview and Value Proposition
GigaSpaces eXtreme Application Platform (XAP) is an enterprise application virtualization platform that provides a solution for end-to-end scalability of the application and its data under extreme latency and load requirements. XAP is a consolidated platform that combines the GigaSpaces in-memory data grid with a fully elastic application platform for complete application scalability, from the load balancer down to the database. XAP is the only platform that enables end-to-end scalability with a single product, and as a single-product solution, it provides the joint benefits of increased performance and cost reduction. XAP is designed to meet the mission-critical needs of a wide range of businesses, with advanced monitoring and management capabilities, high-level automation of operations, cloud readiness that supports private, public, or hybrid architectures, and complete interoperability: XAP provides a solution for scalability in any environment, language, and API, without dictating a specific development framework or environment. This document outlines the technical foundations of GigaSpaces XAP and the “secret sauce” behind the product‟s unique capabilities, the Space-Based Architecture (SBA).
Scale Up vs. Scale Out
This paper discusses the difference between multi-core concurrency (often referred to as the scale-up model) and distributed computing (often referred to as the scale-out model). While the two models seem similar, in the practical sense they are very different. Only the scale-out model enables leveraging the power of multiple machines while also reducing failure and downtime incidence. However, this approach can also involve increased system overhead. So, is it possible to choose between the two approaches? What factors should be considered? And how does the evolution of multi-core technology affect the need to choose?
NoCAP: Or, Achieving Scalability Without Compromising on Consistency
According to the CAP theorem, it is impossible for a distributed system to have all three CAP properties – consistency (C), availability (A), and partition tolerance (P) – necessitating a choice of only two: Some suggest choosing AP and compromising on consistency. Others suggest CA as a better set of tradeoffs. This paper presents the argument that it is not necessary to completely give up partition tolerance by choosing CA, or consistency by choosing AP. Instead of viewing each CAP property in absolute terms and selecting only two, we can adopt a more relaxed approach that applies various degrees of all three, and compromise on the degree in which we apply each property based on the application’s business requirements. In other words, address the most likely failure and network partition scenarios, and compromise only in areas where they are less likely to occur. A common GigaSpaces clustering topologies is used as a reference for this model, with a detailed illustration of how the topology applies to all three CAP properties.
Achieving Read/Write Scale Without A Complete Rewrite - A Customer Case Study: Avanza Bank
Many applications are built with layers upon of layers of development, with relational databases at the heart of the system. Scaling these systems is extremely challenging, leading many organizations to take the easy route – simply paying more for higher-end hardware and databases. Today, we have reached the point where this approach often does not work, and is simply too expensive to maintain with the advent of cheaper, more scalable alternatives. The case of Avanza Bank presents an excellent example of how it is possible to turn an existing online banking application into a new site that is designed for read/write scaling.
Solutions & Patterns
GigaSpaces Air Transportation & Travel Solution
Can your current air travel management system handle today’s hectic load? Can it handle sudden air traffic changes as a result of weather conditions or unexpected aircraft failure? The impact of irregular airline operations on the daily activities of a carrier can lead to significant losses in profitability. Travel solutions based on GigaSpaces technology can handle irregular operations, ticketing, baggage, reservation, re-accommodation and financial analysis. Everything needed for the air transport industry.
Real-Time Analytics for Big Data - An Alternative Approach
Real-time analytics are becoming part of mainstream system design, with high-profile companies such as Facebook sharing their design and implementation processes, proving that real-time is already a reality. However, most of these designs rest on assumptions that inherently limit the resulting systems, among t hem the idea that memory is unreliable, and that there is only one choice of database. This paper examines the proposition that these assumptions should be challenged, and that by changing them, inherent limitations of real-time analytics systems can be eliminated.
Frost & Sullivan 2015 Global Cloud-Based Virtualization in Industrial Automation New Product Innovation Award
Heavy Reading: NFV MANO - What's Wrong & How to Fix It
This report examines the issues influencing vendor interpretations of NFV MANO and the requirements for greater clarity around MANO functionality in general, and improved capabilities within OpenStack in particular.
Omni-channel Retail and In-Memory Computing
With savvy, empowered smartphone shoppers on the rise, decreasing customer attention, and a critical need for a more personalized cross-channel shopping experience, leading retailers are constantly challenged by the inexorable convergence between the online and offline shopping worlds of today. The omni-channel phenomenon is now critical to the future success of all retail companies. While much has been spoken about the front-end shopping experience, the main challenge continues to be the systems and processes that need to be put in place to enable the complete omni-channel customer experience. The key to such success is a cost-effective infrastructure that can optimally integrate all customer touch points. This paper explains a field-proven strategy on how in-memory computing with the GigaSpaces XAP platform can help integrate a retailer’s core commerce technologies to ensure that their customers have the ability to shop as they wish, ship their purchase in the most convenient way, and ultimately, give retailers the flexibility to fulfill customer demands across any channel.
SBA Concept Paper
Application workload is growing at an increasing pace, making scalability a prime concern of application designers and administrators. In this paper, we define scalability, and show that inherent scalability barriers represent a dead end for today’s tier-based business-critical applications. We argue that in order to survive, these applications must achieve linear scalability, and that the only way to do this is to switch from the tier-based model to a new architectural approach. We suggest a novel approach in which applications are partitioned into self-sufficient processing units, and present Space-Based Architecture (SBA) as a practical implementation of this approach. We demonstrate that SBA guarantees both linear scalability and simplicity for designers, developers and administrators – transforming scalability from dead end to open road.
Scaling Healthcare Applications to Meet Rising Challenges of Healthcare IT
Healthcare IT is now dealing with an explosion of data sources, as well as the increasing regulatory requirements to integrate systems and turn messages into actionable information. Enterprise healthcare applications are now being required to handle millions of messages per day at lower latency than ever to meet the challenge of processing terabytes of data, from various sources, which must be available to multiple processes, making system management increasingly complex. Moreover, the ability to mitigate risk and improve patient outcomes requires gaining meaningful information from these vast data streams in real time.
Scaling Spring Applications in 4 Steps
This paper is a brief guide to scaling Spring-based applications. It shows how to solve well-known problems that crop up when applications begin to scale out across multiple physical machines - a bottleneck in the data tier, a bottleneck in the messaging tier, a bottleneck caused by the tight coupling between business logic, data, and messaging, and a bottleneck caused by the limited methods that exist today to deploy and provision applications across multiple computers. We suggest a number of simple yet innovative steps, which leverage the idea of virtualization to help you release these bottlenecks, but do not require that you change your business logic code or otherwise re-work your application. The steps can be performed individually, as a targeted "cure" for each bottleneck, but together they form a holistic solution that leverages Space-Based Architecture (SBA) to enable true linear scalability for your application.
Migrating from JEE to GigaSpaces
This document details the migration process from a typical JEE tier-based application to a full blown Space-Based Architecture implementation, based on GigaSpaces XAP. It is the result of a project carried out by GigaSpaces in 2008, to determine the basis for comparison between GigaSpaces Space-Based Architecture and the standard JEE Tier-Based Architecture. The project was conducted by Grid Dynamics, an independent consulting and engineering company, hired by GigaSpaces for that purpose.
From Only-SQL to NoSQL to YeSQL
It has now been a good couple of years since the various anti-SQL proponents have gained enough momentum to come together under the wide umbrella of the term NoSQL. And it is clear that we can never go back: the typical relational database architecture is clearly insufficient for today’s data-intensive applications, and the move to distributed architectures. But is the problem in the architecture or the query language? The two are not interchangeable, though frequently confused.
Should Web Apps "Just Say No" to SQL?
The recent inaugural get-together of the NOSQL community indicates a growing anti-SQL sentiment among developers. The NOSQL radicals are not alone: top companies in the Internet world, including Google, Amazon and Facebook, are basing their web applications on alternatives to the traditional SQL database. In this paper I'll briefly review what is driving this trend, survey alternative approaches to SQL databases, and discuss not only their benefits but also the risks and caveats for real-life web applications. Ending the paper is a thought exercize showing how Twitter's widely-publicized scalability problems could be addressed using non-SQL patterns
GigaSpaces XAP and Cisco UCS Joint Solution
This whitepaper provides a detailed description of the joint value proposition behind Cisco Unified Computing Resources (UCS) and GigaSpaces eXtreme Application Platform (XAP). The executive summary provides an overview of the joint solution and value proposition. The remainder of the paper provides technical architects and engineers with an in-depth technical review of the joint solution.
Designing a Scalable Twitter with Space-Based Architecture
Everybody knows Twitter – the popular online service that allows people to send short messages to their friends and colleagues. If you think about it, Twitter exposes a few very difficult scalability problems that are common to most real-time or social web applications. If you can design a scalable Twitter, you’re already half-way to solving the scalability issues of most modern web applications. This paper shows you how to do just that.
Social E-Commerce - An Architecture Case Study
A case study of the architecture used by Delver/Sears, of how Sears built a social e-commerce solution that can handle complex relationship queries in real time. The case study includes the architectural considerations behind their solution , why they chose memory over disk, how they partitioned the data to gain scalability, why they chose to execute code with the data using the GigaSpaces Map/Reduce execution framework, how they integrated with Facebook, and why they chose GigaSpaces over Coherence and Terracotta.