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Nice article from Matt Asay, who is now the COO at Canonical, the company behind Linux Ubuntu and Google's Chrome OS. excerpt:  As ZDNet's Dana Blankenhorn remarks, "Just as the Internet takes friction out of the distribution and development process, open source for Google removes friction from the business process." In Android land, this means making it easy for device manufacturers and wireless telecoms to evaluate, develop on, and ship Android-based devices. And ship them they are, to the tune of 60,000 Android devices per day. As Wired noted after the recent Mobile World Congress: This year at the Mobile World Congress is the year of Android. Google's operating system debuted here two years ago....This year, Android is everywhere, on handsets from HTC, Motorola, Sony Ericsson, and even Garmin-Asus. If this were the world of computers, Android would be in a similar position to Windows: Pretty much every manufacturer puts it on its machines. There is one key distinction, though: Android is open source. It makes all the difference.

Excellent Hadoop/Hive explanation.  Hat tip to Matt Asay for the link.  I eft a comment on Matt's blog questioning the consequences of the Oracle vs. Google Android lawsuit, and the possible enforcement of the Java API copyright claim against Hadoop/Hive.  Based on this explanation of Hadoop/Hive, i'm wondering if Oracle is making a move to claim the entire era of Big Data Cloud Computing?  To understand why, it's first necessary to read Matt the Hadoople's explanation.   kill shot excerpt: "You've built your Hadoop job, and have successfully processed the data. You've generated some structured output, and that resides on HDFS. Naturally you want to run some reports, so you load your data into a MySQL or an Oracle database. Problem is, the data is large. In fact it's so large that when you try to run a query against the table you've just created, your database begins to cry. If you listen to its sobs, you'll probably hear "I was built to process Megabytes, maybe Gigabytes of data. Not Terabytes. Not Perabytes. That's not my job. I was built in the 80's and 90's, back when floppy drives were used. Just leave me alone". "This is where Hive comes to the rescue. Hive lets you run an SQL statement against structured data stored on HDFS. When you issue an SQL query, it parses it, and translates it into a Java Map/Reduce job, which is then executed on your data. Although Hive does some optimizations, in general it just goes record by record against all your data. This means that it's relatively slow - a typical Hive query takes 5 or 10 minutes to complete, depending on how much data you have. However, that's what makes it effective. Unlike a relational database, you don't waste time on query optimization, adding indexes, etc. Instead, what keeps the processing time down is the fact that the query is run on all machines in your Hadoop cluster, and the scalability is taken care of for you." "Hive is extremely useful in data-warehousing kind of scenarios. You would

Eucalyptus, an open-source platform that implements "infrastructure as a service" (IaaS) style cloud computing, aims to take open source front and center in the cloud-computing craze. The project, founded by academics at the University of California at Santa Barbara, is now a Benchmark-funded company with an ambitious goal: become the universal cloud platform that everyone from Amazon to Microsoft to Red Hat to VMware ties into. [Eucalyptus] is architected to be compatible with such a wide variety of commonly installed data center technologies, [and hence] provides an easy and low-risk way of building private (i.e. on-premise or internal) clouds...Thus data center operators choosing Eucalyptus are assured of compatibility with the emerging application development and operational cloud ecosystem while attaining the security and IT investment amortization levels they desire without the "fear" of being locked into a single public cloud platform.