May 25, 2014

Decomposing Applications for Deployability and Scalability
One way to think about microservice architecture is that it’s SOA without the commercialization and perceived baggage of WS* and ESB. Despite not being an entirely novel idea, the microservice architecture is still worthy of discussion since it is different than traditional SOA and, more importantly, it solves many of the problems that many organizations currently suffer from. In this article, you will learn about the motivations for using the microservice architecture and how it compares with the more traditional, monolithic architecture. We discuss the benefits and drawbacks of microservices. You will learn how to solve some of the key technical challenges with using the microservice architecture including inter-service communication and distributed data management.

10 things statistics taught us about big data analysis
Many cool ideas in applied statistics are really relevant for big data analysis. So I thought I'd try to answer the second question in my previous post: "When thinking about the big data era, what are some statistical ideas we've already figured out?" Because the internet loves top 10 lists I came up with 10, but there are more if people find this interesting. Obviously mileage may vary with these recommendations, but I think they are generally not a bad idea.

Facebook Moves to Stop Over-Sharing
Now, though, new accounts will be automatically set to only share with friends. The user can then change that if they want to. Facebook also said it plans to remind current users that they may want to rethink who can see their posts. "For people already on Facebook, we've also received the feedback that they are sometimes worried about sharing something by accident, or sharing with the wrong audience," Facebook noted. "Over the next few weeks, we'll start rolling out a new and expanded privacy checkup tool, which will take people through a few steps to review things like who they're posting to, which apps they use, and the privacy of key pieces of information on their profile."

Why a medical doctor decided to join IBM Research
With the number of examinations and tests increasing dramatically from year to year, and the number of MDs specializing in radiology going down, we need to help radiologists work with greater volumes while maintaining diagnostic quality and accuracy.  The use of imaging will become ever more critical as the use of smart contrast materials becomes more popular in diagnosis. For example, if we see a shadow in the lungs, we can’t always differentiate between an infection and a growth. With more accurate visual aids and smarter materials, we’ll be able to get a more accurate diagnosis without doing a biopsy.

Deploying SQL Server 2014 with Cluster Shared Volumes
With traditional cluster storage, each SQL instance requires a separate LUN to be carved out. This because the LUN would need to failover with the SQL instance. CSV allows nodes in the cluster to have shared access to storage. This facilitates the consolidation of SQL instances by storing multiple SQL instances on a single CSV. Consolidating multiple SQL instances on a single LUN makes the storage utilization more efficient.  Traditionally, the number of SQL instances that can be deployed on a cluster is limited to the number of drive letters (24 excluding the system drive and a drive for a peripheral device). There is no limit to the number of mount points for a CSV. Therefore, scalability of your SQL deployment is enhanced.

Will Intel Corporation’s Moorefield Be a Game Changer?
Intel's Moorefield looks pretty good from a CPU and graphics performance perspective, and it will likely be quite competitive with the best from Qualcomm on those fronts. However, when it comes to imaging performance, the Qualcomm and NVIDIA chips handily outpace the Intel processor. The good news is that Intel has its graphics and CPU performance/power stories down pat (something that bears usually cite as an Intel weaknesses in mobile), but the bad news is that Moorefield still isn't quite best in class when all vectors are considered. The relatively poor imaging performance will limit Moorefield's penetration in higher-end smartphones, but shouldn't be a hindrance in cellular-enabled Android tablets.

Can Predictive Analytics Prevent Mischief In Corporate Finance?
If startup Aviso has their way, sprawling corporate enterprises will turn to analytics dashboards and application programming interfaces (APIs) to handle their financial metrics. The company, which just left exited stealth mode last month, is backed by $8 million in Series A funding from Shasta Ventures, Bloomberg BETA, and several other investors. Their goal? Creating a dashboard that lets large companies understand their finances in real time--before quarterly reports are issued. K.V. Rao, the service’s cofounder and CEO, is best known for being the brains behind enterprise automation firm Zuora. During a phone conversation, he told me that Aviso’s mission statement is to “democratize quant science for the enterprise”

These 3 hot new trends in storage will blow your mind! Okay, maybe not quite. (2/2)
There were some rumblings in the Twittersphere about how knowing your competitor and not hiding them behind “Competitor A” or the like was invoking fear, uncertainty, and doubt (FUD). And while it is a conservative, and acceptable, option not to name a competitor if you have a lot of them–Veeam chose this path in their comparisons, for example–that doesn’t mean that it’s automatically deceptive to give a fair and informed comparison within your competitive market. If Dave Wright had gone in front of the delegates and told us how bad all the competitors were and why they couldn’t do anything ri

Data Modeling in Graph Databases
Data modelling consists of using the property graph primitives — nodes, relationships, properties and labels  to build an application-specific graph data model that allows us to easily express the questions we want to ask of that application's domain. When building an application with Neo4j, we typically employ a multi-step process, which starts with a description of the problem we're trying to solve and ends with the queries we want to execute against an application-specific graph data model. This process can be applied in an iterative and incremental manner to build a data model that evolves in step with the iterative and incremental development of the rest of the application.

Enterprise Architecture & Avoiding tunnel vision
What I mean by “Tunnel Vision” is that the architect only looks at what is right in front of him/her (e.g.: The current task/project) , and does not consider the implications of how the decisions being made for this task may impact the wider I.T infrastructure and customer from a commercial / operational perspective. In my previous role I saw this all to often, and it was frustrating to know the solutions being designed and delivered to the customers were in some cases quite well designed when considered in isolation, but when taking into account the “Big Picture” (or what I would describe as the customers overall requirements) the solutions were adding unnecessary complexity, adding risk and increasing costs, when new solutions should be doing the exact opposite.

Quote for the day:

“Leaders who won't own failures become failures.” -- Orrin Woodward