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Important: please read the equipment requirements at the bottom of this page before attending the tutorial
Data has always been a second class citizen on the web. As images, then audio, then video made their way onto the internet, data was always left out of the party, forced into dusty Excel files and ugly HTML tables. Tableau Public is one of the tools aiming to change that by allowing anyone to create interactive charts, maps and graphs and publishing to the web—no programming required.
In this tutorial you will learn why data is vital to the future of the web, how Tableau Public works, and gain hands-on experience with taking data from numbers to the web.
Through three different use cases, you will learn the capabilities of the Tableau Public product. The tutorial will conclude with an extended hands-on session covering the visualization process from data to publishing. Topics covered will include:
IMPORTANT: EQUIPMENT REQUIREMENTS
This is a hands-on tutorial. You will need to bring either a Windows laptop or a laptop with a Windows virtual machine installed. Before arriving, you should download and install Tableau Public from this URL: http://www.tableausoftware.com/p...
by Eddie Satterly and Sanjay Mehta
Organizations today are generating data at an ever-increasing velocity but how can they leverage this Big Data? In this session, Expedia, one of the world’s leading online travel companies, describes how they tapped into their massive machine data to deliver unprecedented insights across key IT and business areas – from ad metrics and risk analysis, to capacity planning, security, and availability analysis.By using Splunk to harness their data, Expedia saved tens of millions of dollars andfreed up key resources who now can focus on innovation instead of just operations.
This session is sponsored by Microsoft
These days users won’t tolerate slow applications. More often than not, the database is the bottleneck in the application. To solve this many people add a caching tier like memcache on top of their database. This has been extremely successful but also creates some difficult challenges for developers such as mapping SQL data to key-value pairs, consistency problems and transactional integrity. When you reach a certain size you may also need to shard your database, leading to even more complexity.
VMware vFabric SQLFire gives you the speed and scale you need in a substantially simpler way. SQLFire is a memory-optimized and horizontally-scalable distributed SQL database. Because SQLFire is memory oriented you get the speed and low latency that users demand, while using a real SQL interface. SQLFire is horizontally scalable, so if you need more capacity you just add more nodes and data is automatically rebalanced. Instead of sharding, SQLFire automatically partitions data across nodes in the distributed database. SQLFire even supports replication across datacenters, so users anywhere on the globe can enjoy the same fast experience.
Stop by to learn more how SQLFire gives high performance without all the complexity.
This session is sponsored by VMware
MapReduce, Hadoop, and other “NoSQL” big data approaches open opportunities for data scientists in every industry to develop new data-driven applications for digital marketing optimization and social network analysis through the power of iterative, big data analysis. But what about the business user or analyst? How can they unlock insights through standard business intelligence (BI) tools or SQL access? The challenge with emerging big data technologies is finding staff with the specialized skill sets of the data scientist to implement and use these solutions. Business leaders and enterprise architects struggle to understand, implement, and integrate these big data technologies with their existing business processes and IT investments and provide value to the business. This session will explore a new class of analytic platforms and technologies such as SQL-MapReduce® which bring the science of data to the art of business. By fusing standard business intelligence and analytics with next-generation data processing techniques such as MapReduce, big data analysis is no longer just in the hands of the few data science or MapReduce specialists in an organization! You’ll learn how business users can easily access, explore, and iterate their analysis of big data to unlock deeper sights. See example applications with digital marketing optimization, fraud detection and prevention, social network and relationship analysis, and more.
This session is sponsored by Teradata Aster
by Tim Estes
Data Scientists deal with a complex world of Big Data – increasing volume, velocity and variety of data – demanding an evolution in the solutions for analytics. Analytics today are not just about statics but really understanding the meaning of content regardless of the source or the structure. This is even more the case with unstructured data. While unstructured data has been a major issue in the area of Intelligence and National Security, its now a mainstream problem with the overwhelming amount of information that users and business most face every day from Social Media and Online content. We can’t just search or count anymore- it is vital to create and make sense of the valuable interconnections of entities and relationships that are key to our daily decisions. Tim will introduce Automated Understanding for Big Data and explain how this new evolution is the fundamental step in the next wave of software. Tim will show the power of this new capability on a large and valuable dataset that has never been deeply understood by software before.
This session is sponsored by Digital Reasoning
Attendees of this session will learn, among other things, how to handle component failures in a complex distributed system. The cloud monitoring team uses geographic redundancy and isolation along with an innovative build process to create a system where failures can be quickly detected and addressed by the team.
They can also expect to learn how to understand and cope with the relational/non-relational impedance. There are data modeling anti-patterns that are easy to fall prey to when coming from a relational background, and the right approaches are often not intuitive.
Finally, attendees will hear on how to make open source work. Many open source projects suffer from poor documentation and support, or companies that offer support at exorbitant prices. Understanding how open source communities work and employing engineers familiar with open source software makes it easier to leverage these projects.
Social data is growing, Twitter produces 250+ million tweets per day and 27 million links to news and media. Big Data can give insights into these large datasets but first the data must be curated, cleaned and quantified before it has value. We will cover how we move from unstructured to structured and how we take simple data and apply complex processes to give context to the data.
We will cover how we developed a platform that can deal with billions of items per day and perform complex analysis before handing the data onto thousands of customers in real-time. We will also walk through our platform architecture looking at our use of Hadoop, HBase, 0MQ, Kafka and many other cutting edge technologies. You will learn some of the pitfalls of running a production Hadoop cluster and the value when you make it work.
by Gary Lang
Gary Lang, Senior VP Engineering, MarkLogic, will discuss the concept of Big Data Applications and walk through three in-production implementations of Big Data Applications in action. These applications include how LexisNexis built a next-generate search application, how a major financial institution simplified its technology infrastructure for managing complex derivative trades, and how a major movie studio implemented an Enterprise Data Layer for access to all of their content across multiple silos.
This session is sponsored by MarkLogic
by Max Yankelevich
The founder and CEO of CrowdControl Software, Max Yankelevich is going to explore new ways to solve big data problems involving crowdsourcing. He will define crowdsourcing and the common barriers to applying it to Big Data. Everyone knows managing the crowd can be a nightmare given the complexity involved and the quality issues that arise. Many companies focus on the quantity of data when often times it’s the quality that really matters. Through years of research at MIT and in collaboration with Amazon Mechanical Turk, Yankelevich has created an artificial intelligence application that maximizes the quality of crowdsourced work at scale. He will cover specifc company use cases for collecting, controlling, correcting and enriching data. From startups to Fortune 500 companies, this new methodology is transforming data driven businesses. Its applications range from human sentiment analysis to keeping business listings up to date.
This session is sponsored by CrowdControl Software
28th February to 1st March 2012