WTF is Elastic Data Grid? (By Example)

Forrester released their new wave report:  The Forrester Wave™: Elastic Caching Platforms, Q2 2010 where they listed GigaSpaces, IBM, Oracle, and Terracotta as leading vendors in the field. In this post I'd like to take some time to explain what some of these terms mean, and why they’re important to you. I’ll start with a definition of Elastic Data Grid (Elastic Caching), how it is different then other caching and NoSQL alternatives, and more importantly -- I'll illustrate how it works through some real code examples.

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Web Speed Can Push You Off of Google Search Rankings! What Can You Do?

Google made a right move by adding web-speed to the search engine ranking. With this change site latency does't just impact the user experience but it will determine where you will be placed in google search results.

This move could be a real game changer as it bring site latency and scalability to the front stage. Sites will not only compete on content but on thier performance. It is now clear that site performance have much more direct impact on our business (as appose to indirect impact resulted in user expereince) then ever before.

In this post i try to provide some architecture guide line on how to control and improve site latency under scale based on a discussion with a leading eCommerce site the Netherland.

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Elasticity for the Enterprise -- Ensuring Continuous High Availability in a Disaster Failure Scenario

Many enterprises' high-availability architecture is based on the assumption that you can prevent failure from happening by putting all your critical data in a centralized database, back it up with expensive storage, and replicate it somehow between the sites. As I argued in one of my previous posts (Why Existing Databases (RAC) are So Breakable!) many of those assumptions are broken at their core, as storage is doomed to failure just like any other device, expensive hardware doesn’t make things any better and database replication is often not enough.

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The Missing Piece in the Virtualization Stack (Part 1)

This and the next post will discuss how virtualization and cloud computing, as we know it today, is only a small part of the solution for today’s IT inefficiencies. While new technologies and delivery models have made it much simpler to manage the infrastructure, this is not where our core inefficiencies lie. Virtualization principles must be extended to higher levels of the application stack, to make it easier for all of us to manage, tune and integrate applications. Otherwise we will continue to spend most of our time on things that don’t provide real value to the business.

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The Common Principles Behind the NOSQL Alternatives

This post draws some of the common patterns behind the various NOSQL alternatives, and how they address the database scalability challenge.

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Why Existing Databases (RAC) are So Breakable!

One of the core assumption behind many of today’s databases is that disks are reliable. In other words, your data is “safe” if it is stored on a disk, and indeed most database solutions rely heavily on that assumption. Is it a valid assumption?

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Is Your Data Really Secured?

Caching/data-grids are going through a similar evolution to databases. As with databases, we started by using caching as an embedded service to the application. Now we are in the phase where we need to be able to share the data between multiple applications, or in cases where we don’t want to share the data, we need to be able to share the resources for managing the data, while keeping a high degree of isolation.

The demand for these sort of requirements becomes much more common with SOA or SaaS-based applications. As we approach the next generation of middleware and data-centers, it becomes clear that we cannot move to the next wave of virtualization and cloud computing without a strong security and isolation solution that is built-in to all layers of our application and middleware.