GOTO is a vendor independent international software development conference with more that 90 top speaker and 1300 attendees. The conference cover topics such as .Net, Java, Open Source, Agile, Architecture and Design, Web, Cloud, New Languages and Processes
Dean Wampler, TweetBig Dataist, O'Reilly Author
Biography: Dean Wampler
Dean Wampler is a Principal Consultant for Think Big Analytics, specialists in “Big Data” application development, primarily using Hadoop-related technologies. Dean is a contributer to several open-source projects and the founder of the Chicago-Area Scala Enthusiasts. He is the author of Functional Programming for Java Developers, the co-author of Programming Scala, and the co-author of Programming Hive, all from O’Reilly. He pontificates on twitter and at polyglotprogramming.com.
Twitter: @deanwampler
Presentation: TweetWhat’s Ahead for Big Data?
Time:
Tuesday 11:00 - 11:50
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Location:
Walton South
Apache Hadoop, based on MapReduce, and NoSQL databases are the current darlings of the Big Data world. The MapReduce computing model decomposes large data-analysis jobs into smaller tasks that are distributed around a cluster. MapReduce itself was pioneered at Google for indexing the Web and other computations over massive data sets. For cost-effective storage at scale, NoSQL databases offer various storage models and availability vs. consistency trade-offs.
The strengths of MapReduce are cost-effective scalability and relative maturity. Its weaknesses are its batch orientation, making it unsuitable for real-time event processing, and the inflexibility of the MapReduce computing model.
Storm is emerging as a popular complement to Hadoop for general event processing. NoSQL databases can meet some event-processing requirements, too.
The challenges of programming with MapReduce are best addressed with higher-level APIs and Functional Programming languages that provide common query and data-manipulation abstractions, making it easier to implement MapReduce programs. However, longer term, we need new distributed computing models that are more flexible for different problems and which provide better real-time performance. Besides Storm, Spark is a new, general-purposes system for distributed computing. Graph systems, such as Google’s Pregel, address problems that are best handled with graph algorithms.
Similarly, the NoSQL world is changing. Even as the established players mature, newer entrants to the market reflect the lessons learned from the pioneers, including a renewed interest in the Relational Model!
I’ll examine the current Big Data landscape, discuss where it’s going in the near term, and speculate about the future. I’ll make the case that Big Data is essentially Applied Mathematics, a “killer app” for Functional Programming. To succeed, Big Data systems must embrace this essential truth.
The strengths of MapReduce are cost-effective scalability and relative maturity. Its weaknesses are its batch orientation, making it unsuitable for real-time event processing, and the inflexibility of the MapReduce computing model.
Storm is emerging as a popular complement to Hadoop for general event processing. NoSQL databases can meet some event-processing requirements, too.
The challenges of programming with MapReduce are best addressed with higher-level APIs and Functional Programming languages that provide common query and data-manipulation abstractions, making it easier to implement MapReduce programs. However, longer term, we need new distributed computing models that are more flexible for different problems and which provide better real-time performance. Besides Storm, Spark is a new, general-purposes system for distributed computing. Graph systems, such as Google’s Pregel, address problems that are best handled with graph algorithms.
Similarly, the NoSQL world is changing. Even as the established players mature, newer entrants to the market reflect the lessons learned from the pioneers, including a renewed interest in the Relational Model!
I’ll examine the current Big Data landscape, discuss where it’s going in the near term, and speculate about the future. I’ll make the case that Big Data is essentially Applied Mathematics, a “killer app” for Functional Programming. To succeed, Big Data systems must embrace this essential truth.
Workshop: The Seductions of Scala Tweet
Time:
Friday 09:00 - 16:00
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Location:
Training 3
I was seduced by the Scala language several years ago. This hands-on tutorial will show you why. We’ll see how Scala fixes many issues with Java’s object model and type system. We’ll learn how its powerful Functional Programming features improve productivity, quality, concurrency, and eliminate many of the common examples of over-engineering seen in typical “enterprise” applications today. Scala is unusually good at supporting Domain Specific Languages (DSLs) and its higher-level concurrency features, such as the Akka Actor system, make distributed systems far easier to implement reliably.
Even if you don’t use Scala in your daily work, the ideas you’ll learn will alter the code you write in any language, for the better.
Target Audience
This tutorial will appeal to developers interested in functional programming and new languages on the JVM, especially for building distributed systems.
I will assume that you already know other programming languages and that you are comfortable writing code for this hands-on tutorial. Bring your laptop with Scala V2.10 installed and your favorite text editor or IDE.
Even if you don’t use Scala in your daily work, the ideas you’ll learn will alter the code you write in any language, for the better.
Target Audience
This tutorial will appeal to developers interested in functional programming and new languages on the JVM, especially for building distributed systems.
I will assume that you already know other programming languages and that you are comfortable writing code for this hands-on tutorial. Bring your laptop with Scala V2.10 installed and your favorite text editor or IDE.