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During the RavenDB courses* in the past few weeks, I was talking with one of the attendees and I came up with what I think is a great analogy. * Yes, courses, in the past 4 weeks, I’ve given the RavenDB course 3 times. That probably explains why I don’t remember which course it was. What are your success metrics? From Opening a Champagne Bottle To Hiding Under the Bed with Said Bottle? The first success  metric is when you have enough users (and, presumably, revenue) to cross the threshold to the Big Boys League. Let us call this
the 25,000 users range.  That is the moment when you throw a party, go to the store and grab a whole case of champagne bottles and make fancy speeches. Of course, the problem with success is that you can have too much of it. A system that does just fine (maybe creeks a little ) on a 25,000 users is going to behave pretty differently when you have 100,000 users. That is the moment when you find your engineers under the bed, with a half empty bottle of champagne and muttering things about Out Of Capacity errors and refusing to...(Read whole news on source site)

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Software TypeScript 1.0 Released and Open for Contributions - Polita Paulus shares an official update on the TypeScript project, discussing the 1.0 release and the opensourcing and accepting of community contributions to the TypeScript project. Unity 3.5 RTW: Now with more Peace, Love, and Rock ‘n’ Roll - Grigori Melnik announces the release of version 3.5 of [...]

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Both R language and esProc have the outstanding ability to perform the stepwise computations. However, in the particulars they differ from each other. A comparison between them will be done by the following example:A company’s Sales department wants to select out the outstanding salespersons through statistics, that is, the salespersons whose sales amounts are always among the top 3 in each month from the January this year to the previous year. The data is mainly from the order table of MSSQL database: salesOrder, and the main fields include the ID of order: ordered, name of salesperson: name,
sales amount: sales, and date of order: salesDate.The solution is like this substantially:1. Compute the beginning dates of this year and this month, and filter the data by date.2. Group by month and salesperson, and compute the sales amount of each salesperson in each month.3. Group by month, and compute the rankings of sales amount in each group.4. Filter out the top 3 salespersons from each group.5. Compute the set of intersections of each group, that is, salespersons always among the top 3 in each month.The solution of R language is as...(Read whole news on source site)

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Originally posted on: http://geekswithblogs.net/TATWORTH/archive/2014/04/22/apress-deal-of-the-day---22apr2014---sql-server.aspx Today's $10 deal of the day from APress at http://www.apress.com/9781430247913 is SQL Server 2012 Data Integration Recipes "SQL Server 2012 Data Integration Recipes provides focused and practical solutions to real world problems of moving data in and out of SQL Server, helping to create robust and resilient ETL environments. "

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