You love MySQL for its ease of deployment – but are you worried about how your application will perform when it starts to scale?

SPEAKER: Gerry Narvaja, Tokutek
DATE: Wednesday, November 6th
TIME: 1pm ET
Register Now!

Join this interactive webinar with Gerry Narvaja of Tokutek as he walks through the potential pitfalls when using MySQL for Big Data applications, how you can avoid unnecessary tolls on time and resources and tips on how to get the most out of your MySQL applications with open source TokuDB.

Attend this webinar to learn how to:

• dramatically increase performance without having to rewrite code
• reduce the total cost of your servers and flash/SSD storage
• perform hot schema changes

The …

The second article in a series covering Big Data and SQL interaction is available now:

“Big data” is a term that has been used regularly now for almost a decade, and it — along with technologies like NoSQL — are seen as the replacements for the long-successful RDBMS solutions that use SQL. Today, DB2®, Oracle, Microsoft® SQL Server MySQL, and PostgreSQL dominate the SQL space and still make up a considerable proportion of the overall market. Here in Part 2, we will concentrate on how to use HBase and Hive for exchanging data with your SQL data stores. From the outside, the two systems seem to be largely similar, but the systems have very different goals and aims. Let\’s start by looking at how the two systems differ and how we can take advantage of that in our big data requirements.

SQL to Hadoop and back again, Part 2: …

“The most valuable thing I’ve learned in this role is that judicious use of a little bit of knowledge can go a long way. I’ve seen colleagues and other companies get caught up in the “Big Data” craze by spend hundreds of thousands of pounds sterling on a Hadoop cluster that sees a few megabytes [...]

Ask ten DBAs for a definition of ‘Big Data’ and you well get more than ten replies. And the majority of those replies will lead you to Hadoop. Hadoop has been the most prominent of the big data frameworks in the open source world. Over 80% of the Hadoop instances in the world are feed their data from MySQL1. But Hadoop is made up of many parts, some confusing and many that do not play nicely with each other. It is analogous to being given a pile of automotive parts from different models and tyring to come up with a car at the end of the day. So what if you do if you are wanting to copy some of your relational data into Hadoop and want to avoid the equivilent of scraped knuckles? The answer is Bigtop and what follows is a way to get a one node does all system running so you can experiement with Hadoop, Map/Reduce, Hive, and all the other parts.

Bigtop is an Apache Project self …

Sorry for this delay in providing part 2 of this series, but stuff happened that had really high priority, and in addition I was on vacation. But now I'm back in business!

So, last time I left you with some open thought on why Big Data can be useful, but that we also need new analysis tools as well as new ways of visualizing data for this to be truly useful. As for analysis, lets have a look at text, which should be simple enough, right? And sometimes it is simple. One useful analysis tool that is often overlooked is Google. Let's give it a shot, just for fun: if I think of two fierce competitors, somehow, that we can compare, say Oracle and MySQL.. Oracle is much older, both as a technology and as a company and in addition owns the MySQL brand these days. But on the other hand, the Web is where MySQL has it's sweet spot. Just Googling for MySQL and Oracle shows that MySQL seems to be much more discussed (and no, I haven't turned …

I will be talking about Big Data with MySQL and Hadoop at MySQL Connect 2013 (Sept. 21-22) in San Francisco as well as at Percona University at Washington, DC (September 12, 2013). Apache Hadoop is a very popular Big Data solution and we can nowadays easily integrate it with MySQL. I will start with a brief introduction of Apache Hadoop and its components (HFDS, Map/Reduce, Hive, HBase/HCatalog, Flume, Scoop, etc). Next I will show 2 major Big Data scenarios:

• From file to Hadoop to MySQL. This is an example of “ELT” process: Extract data from external source; Load data into Hadoop; Transform data/Analyze data; Extract results to MySQL. It is similar to the original Data Warehouse ETL …

This is the first blog post in a series where I hope to raise a bit above the technical stuff and instead focus on how we can put Big Data to effective use. I ran a SkySQL Webinar on the subject recently that you might also want to watch, and a recording is available here:http://bit.ly/17TTQnJ

Yes, so what? Why do you need or want all that data? All data you need from your customers you have in your Data Warehouse, and all data you need on the market you are in, you can get from some analyst? Right?

Well, yes, that is one source of data, but there is more to it than that. The deal with Data is that once you have enough of it, you can start to see things you haven't seen before. Trend analysis is only relevant when you have enough data, and the more you have, the more accurate it gets.Big Data is different from the data you already have in that it is Bigger, …

” One of the biggest challenges with any project of such a long duration is coping with change. There are many aspects to coping with change, including changes in requirements, changes in technology, vendor stability, changes in staffing and so on”–Jon Brumfitt. On May 14, 2009, the European Space Agency launched an Arianne 5 rocket [...]

Those who deal with big data probably know about Disco – a distributed computing framework aimed to provide a MapReduce platform for big data processing Python applications. We are proud to say that we are one of the largest users of Disco in the Netherlands. As an owner of multiple high-traffic portals with lots of […]

The post Big data processing with Disco appeared first on Spil Games Engineering.

“We went down the path of building Oracle NoSQL database because of explicit request from some of our largest Oracle Berkeley DB installations that wanted to move away from maintaining home grown sharding implementations and very much wanted an out of box technology that can replicate the robustness of what they had built “out of [...]