Looking forward to Paul Miller’s reprise of the ‘short form’ Cloud Computing Channel.  

cloudofdata:

Back in 2011, I spent a year curating GigaOM Pro’s Cloud Computing channel. The ‘Today in Cloud’ piece was something I really enjoyed doing, and valued; 100 words or thereabouts, on a topical issue, trend, news release, or whatever.

As the Cloud channel’s pool of long-form content broadened…

19 May 2 cloudofdata Permalink

Excellent post from Andy Thurai, the Chief Architect & Group CTO for the Intel unit that is responsible for Cloud/ Application security, API, Big Data, SOA and Mobile middleware solutions.

You can follow him @AndyThurai (Twitter) or at thurai.net.

Now-a-days every single CIO, CTO, or business executive that I speak to is captivated by these three new technologies: Big Data, API management and IoTs (Internet of Things). Every single organizational executive that I speak with confirms that they either have current projects that are actively using these technologies, or they are in the planning stages and are about to embark on the mission soon.

Though the underlying need and purpose served are unique to each of these technologies, they all have one thing common. They all necessitate newer security models and security tools to serve any organization well.  

…

18 May Permalink

Chris Twigg, myvirtualplayground.co.uk

‘In 2003, we decided to investigate the dynamics behind editing in Wikipedia. History flow is the method we invented to make sense of the data we collected.’

17 May Permalink

Operational analytics is making headlines in 2013. But why is it important? And why is it more likely to succeed now than in the mid-2000s, when it was called operational BI, or the mid-1990s when it surfaced as the operational data store (ODS)?

First, let’s define the term. My definition, from two recent white papers (April 2012 and May 2013) is: “Operational analytics is the process of developing optimal or realistic recommendations for real-time, operational decisions based on insights derived through the application of statistical models and analysis against existing and/or simulated future data, and applying these recommendations in real-time interactions.” While the language is clearly analytical in tone, the bottom line of the desired business impact is much the same as definitions we’ve seen in the pact for the ODS and operational BI: real-time or near real-time decisions embedded into the operational processes of the business.

17 May Permalink

Interesting post by Arun Murthy on Apache Hadoop 2.0 and YARN.  He makes an interesting case about enabling SQL IN Hadoop rather than ON TOP of Hadoop.  Very interesting to see Hadoop 2.0 characterized as a ‘multi-application operating system.’

Fast forward to today, and we see that Hadoop’s momentum has continued and many more enterprises (not just web scale companies) want to store ALL incoming data in Hadoop, and then enable their users to interact with it in a host of different ways: batch, interactive, analyzing data streams as they arrive, and more. And most importantly, they need to be able to do this all simultaneously without any single application or query consuming all of the resources of the cluster to do so.

Nothing illustrates this dynamic more clearly than the current industry noise around SQL on Hadoop. All kinds of vendors are clamoring to provide better SQL access to data stored in Hadoop – and so they should, since SQL is understood by many users. Since Apache Hive has been the defacto SQL interface to Hadoop data for many years, we’ve found most users would like to continue to leverage the power of Hive in support of these additional interactive SQL use cases.

But by building SQL access on top of Hadoop, it just highlights the challenge of Hadoop being a single application system. For when I run a SQL query on that data, it could consume all the resources of the cluster and cause performance issues for the other applications and jobs running in the cluster – not a good outcome to say the least.

16 May Permalink

nosql:

Urs Hölzle in a post summarizing some of the announcements at Google I/O:

Google Cloud Datastore is a fully managed and schemaless solution for storing non-relational data. Based on the popular App Engine High Replication Datastore, Cloud Datastore is a standalone service that features automatic scalability and high availability while still providing powerful capabilities such as ACID transactions, SQL-like queries, indexes and more.

I’m heading over to the project’s site to read more.

Original title and link: Introducing Google Cloud Datastore (NoSQL database©myNoSQL)

16 May 12 nosql Permalink