Past Meetup

Big Data is a Big Deal - Running Hadoop, Functional Code, Machine Learning

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217 people went

Details

Two Talks

• Talk 1 - Big Data is a Big Deal - Running Hadoop in the cloud

• Talk 2 - Machine learning on .NET: F# FTW

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Talk 1 - Big Data is a Big Deal - Running Hadoop in the cloud

You will leave this session being able to understand and explain what Big Data really means. This session was given at Silicon Valley Code Camp to 200 attendees.

This session is designed to give you a solid understanding of underpinnings and principles of Hadoop, perhaps the most sought after high-paying skill for a developer today. The in-depth session will begin by illustrating on how you build your own single node cluster of Hadoop on the Linux virtual machine (CentOS) for free so you can start learning immediately.

The session will begin with a raw Linux VM and all the needed components will be downloaded and installed. You can be up and running quickly in the cloud within 30 minutes. This session will contain code and will show no more than a few slides.

We will learn about writing the low-level map/reduce code in Java, which is really the assembly language of Hadoop code. From there we will introduce more efficient approaches to analyzing big data with Hadoop, running high level queries and analyzing crime information from San Francisco as the example. We will create tables, import data, and group crime types all with a simple SQL like interface that is Hive.

Finally, we will include a brief talk on PIG as well to round out the high level programming models and additional follow up materials so you can be up to speed on one of the most promising and financially rewarding skills today.

Speaker - Bruno Terkaly

During my 10 year career at Microsoft, I've worked with countless customers, providing services such as low-level troubleshooting scenarios, live and post-mortem debugging, on-the-fly application design and code reviews, performance tuning (IIS, SQL Server, .NET), application stability, porting / migration assistance, general development consulting. I have advised numerous CTO’s and CIO’s over the years about the appropriateness of various software technologies. I am a prolific blogger at: http://blogs.msdn.com/brunoterkaly. I’ve been on TV several times, as well as radio, newspaper and other media. I have also a columnist for the industry leading software trade journal called MSDN Magazine. More recently, my team placed 3rd in the most recent Tech Crunch Disrupt Hackathon 2013. Finally, I have some popular apps I have authored in the Windows Store.

Talk 2 - Machine learning on .NET: F# FTW

While Machine Learning practitioners routinely use a wide range of tools and languages, C# is conspicuously absent from that arsenal. Is .NET inadequate for Machine Learning? In this talk, I'll argue that it can be a great fit, as long as you use the right language for the job, namely F#.

F# is a functional-first language, with a concise and expressive syntax that will feel familiar to data scientists used to Python or Matlab. It combines the performance and maintainability benefits of statically typed languages, with the flexibility of Type Providers, a unique mechanism that enables seamless consumption of virtually any data source. And as a first-class .NET citizen, it interops smoothly with C#. So if you are interested in a language that can handle both flexible data exploration and the pressure of a real production system, come check out what F# has to offer!

Speaker - Mathias Brandewinder

Mathias Brandewinder has been developing software on .NET for about 10 years, and loving every minute of it, except maybe for a few release days. His language of choice was C#, until he discovered F# and fell in love with it. He enjoys arguing about code and how to make it better, and gets very excited when discussing TDD or F#. His other professional interests include math, forecasting, machine learning and probability. Mathias is a Microsoft F# MVP and the founder of Clear Lines Consulting. He is based in San Francisco, blogs at http://www.clear-lines.com/blog , and can be found on Twitter as @Brandewinder.