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"Google's Cloud Platform has long featured Cloud SQL, a zero-maintenance MySQL database that's hosted on Google's cloud platform. What it didn't offer was an API to easily manage these databases without having to use Google's admin interface. Today, however, Google is launching the Cloud SQL API. This new REST API will allow developers to programmatically manage their database instances and open a number of new use cases for Cloud SQL. The API, which Google still deems to be experimental, will allow developers to create their own workflows to easily create and delete instances, restart them and restore them from backup. They will also be able to use it to important and export their databases to and from Google Cloud Storage. For developers, this means using Google's cloud database is now quite a bit easier, especially if they need to regularly manage multiple databases for their customers. Google's launch partner for this API is OrangeScape, which uses it to power parts of KiSSFLOW, its Google Apps workflow SaaS service. "

Comment posted 1 week ago in reply to Zzgomes .....  by Ed Carp.  Finally someone who gets it! OBTW, i replaced Windows 7 with Linux Mint over a year ago and hope to never return.  The thing is though, i am not a member of a Windows productivity workgroup, nor do i need to connect to any Windows databases or servers.  Essentially i am not using any Windows business process or systems.  It's all Internet!!! 100% Web and Cloud Services systems.  And that's why i can dump Windows without a blink! While working for Sursen Corp, it was a very different story.  I had to have Windows XP and Windows 7, plus MSOffice 2003-2007, plus Internet Explorer with access to SharePoint, Skydrive/Live.com.  It's all about the business processes and systems you're part of, or must join.   And that's exactly why the Linux Desktop has failed.  Give Cloud Computing the time needed to re-engineer and re-invent those many Windows business processes, and the Linux Desktop might suceed.  The trick will be in advancing both the Linux Desktop and Application developer layers to target the same Cloud Computing services mobility targets.  ..... Windows will take of itself.   The real fight is in the great transition of business systems and processes moving from the Windows desktp/workgroup productivity model to the Cloud.  Linux Communities must fight to win the great transition. And yes, in the end this all about a massive platform shift.  The fourth wave of computing began with the Internet, and will finally close out the desktop client/server computing model as the Web evolves into the Cloud. excerpt: Most posters here have it completely wrong...the *real* reason Linux doesn't have a decent penetration into the desktop market is quite obvious if you look at the most successful desktop in history - Windows.  All this nonsense about binary driver compatibility, distro fragmentation, CORBA, and all the other red herrings that people are talking about are completely irrelevant

Google releases JavaScript alternative Web application programming language.  Release includes Cloud SQL, a cloud computing database to write Web apps against - using either JavaScript or DART. excerpt: Google on Monday introduced a preview version of Dart, its new programming language for Web applications. The introduction was widely expected, not only because the announcement was listed on the GOTO developer conference schedule, but because a Google engineer described the language and its reason for being in a message sent to a developer mailing list late last year. "The goal of the Dash [Dart's former name] effort is ultimately to replace JavaScript as the lingua franca of Web development on the open Web platform," said Google engineer Mark S. Miller in his post last year. More Insights White Papers The Dodd-Frank Act: Impact on Derivatives Technology Infrastructure Simple is Better: Overcoming the complexity that robs financial data of its potential Analytics Mobility's Next Challenge: 8 Steps to a Secure Environment SaaS 2011: Adoption Soars, Yet Deployment Concerns Linger Webcasts Effective IT Inventory and Asset Management: From Quagmire to Quick Fix Outsourcing Security: What Every Potential Cloud Security Customer Should Know Videos In an interview at Interop New York, Cisco's Justin Griffin shows how their wireless products can physically map radio sources by analyzing the spectrum. This allows you to detect rogue devices and sources of interference. Lars Bak, a Google engineer who helped develop Chrome's V8 JavaScript engine and one of the creators of Dart, said in a phone interview that Google works regularly on large Web applications and that the company's engineers feel they need a new programming language to describe large, complex Web applications.

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

Microsoft announcement is at http://www.microsoft.com/Presspass/press/2010/jul10/07-12CloudComputingPR.mspx