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T-SQL Tuesday #027 – The Big Data Valentine’s Edition

February 14, 2012

tsqltuesdayIt’s T-SQL Tuesday time again, and I’m honored to be able to host the party for February 2012. This is my second time hosting, with the first being #013.

T-SQL Tuesday is the brainchild of Adam Machanic, who hosted the very first blog party. If you’d like to be a host, contact Adam on his blog. The complete list of older topics is here: T-SQL Tuesday Topics – February 2012, so you can pick a new one if you’d like to host.

Note, this is advance notice. The party is NOT today, it’s next week, February 14, 2012.

The Rules

Here are the rules your post must follow.

  1. Your post must go live between 00:00:00 GMT on Tuesday February 14, 2012 and 00:00:00 GMT on Wednesday February 15, 2012.
  2. Your post must link back to this post, and the link must be anchored from the logo (found above) which must also appear at the top of your post.
  3. Leave a comment or a trackback here on this blog regarding your post.
  4. ‘T-SQL Tuesday #027′ should be included in the title of the post.
  5. If you’re on Twitter, tweet about your post using the hash tag #TSQL2sDay

Big Data

godivaThe party falls on Valentine’s Day this month, and I thought a little notice would let you get your post done early, and then spend the day with your loved ones

Since the media has a love affair with Big Data right now, I thought this would make an interesting topic. Data is the lifeblood of our careers and of many organizations. Slowly governments, companies, and individuals are becoming aware of just how important data is to us, and as they do, they want more of it. More storage, more access, and more analysis.

That creates challenges for us as data professionals. We will have to learn to better manipulate, aggregate, summarize, and handle larger volumes of data in the future. I think this means we will have lots of employment in this area, and it means tremendous opportunity for those that learn to work with data well.

This month I want to hear what big data problems you’ve solved, or interesting ways of working with big data, challenges that you struggle with, or cool hardware tricks that mask the problems in your code.

Think big, and let us know how you work with Big Data.

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