"strata sc 2014 sessions" entries

Apache Mesos: Open Source Datacenter Computing

Mesos offers reliability, efficiency and faster developer productivity.

Virtual machines (VMs) have enjoyed a long history, from IBM’s CP–40 in the late 1960s on through the rise of VMware in the late 1990s. Widespread VM use nearly became synonymous with “cloud computing” by the late 2000s: public clouds, private clouds, hybrid clouds, etc. One firm, however, bucked the trend: Google.

Google’s datacenter computing leverages isolation in lieu of VMs. Public disclosure is limited, but the Omega paper from EuroSys 2013 provides a good overview. See also two YouTube videos: John Wilkes in 2011 GAFS Omega and Jeff Dean in Taming Latency Variability… For the business case, see an earlier Data blog post, that discusses how multi-tenancy and efficient utilization translates into improved ROI.

One takeaway is Google’s analysis of cluster traces from large Internet firms: while ~80% of the jobs are batch, ~80% of the resources get used for services. Another takeaway is Google’s categorization of cluster scheduling technology: monolithic versus two-level versus shared state. The first category characterizes Borg, which Google has used for several years. The third characterizes their R&D goals, a newer system called Omega.

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An Introduction to Hadoop 2.0: Understanding the New Data Operating System

Sneak peek at an upcoming tutorial at Strata Santa Clara 2014

By Rich Raposa

Apache Hadoop 2.0 represents a generational shift in the architecture of Apache Hadoop. With YARN, Apache Hadoop is recast as a significantly more powerful platform – one that takes Hadoop beyond merely batch applications to taking its position as a ‘data operating system’ where HDFS is the file system and YARN is the operating system.

YARN is a re-architecture of Hadoop that allows multiple applications to run on the same platform. With YARN, applications run “in” Hadoop, instead of “on” Hadoop:


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Data Transformation

Skills of the Agile Data Wrangler

By Joe Hellerstein and Jeff Heer

As data processing has become more sophisticated, there has been little progress on improving the most time-consuming and tedious parts of the pipeline: Data Transformation tasks including discovery, structuring, and content cleaning . In standard practice, this kind of “data wrangling” requires writing idiosyncratic scripts in programming languages such as Python or R, or extensive manual editing using interactive tools such as Microsoft Excel. The result has two significantly negative outcomes. First, people with highly specialized skills (e.g., statistics, molecular biology, micro-economics) spend far more time in tedious data wrangling tasks than they do in exercising their specialty. Second, less technical users are often unable to wrangle their own data. The result in both cases is that significant data is often left unused due to the hurdle of transforming it into shape. Sadly, when it comes to standard practice in modern data analysis, “the tedium is the message.” In our upcoming tutorial at Strata, we will survey both sources and solutions to the problems of Data Transformation.

Analysts must regularly transform data to make it palatable to databases, statistics packages, and visualization tools. Data sets also regularly contain missing, extreme, duplicate or erroneous values that can undermine the results of analysis. These anomalies come from various sources, including human data entry error, inconsistencies between integrated data sets, and sensor interference. Our own interviews with data analysts have found that these types of transforms constitute the most tedious component of their analytic process. Flawed analyses due to dirty data are estimated to cost billions of dollars each year. Discovering and correcting data quality issues can also be costly: some estimate cleaning dirty data to account for 80 percent of the cost of data warehousing projects.

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Six reasons why I recommend scikit-learn

It's an extensive, well-documented, and accessible, curated library of machine-learning models

I use a variety of tools for advanced analytics, most recently I’ve been using Spark (and MLlib), R, scikit-learn, and GraphLab. When I need to get something done quickly, I’ve been turning to scikit-learn for my first pass analysis. For access to high-quality, easy-to-use, implementations1 of popular algorithms, scikit-learn is a great place to start. So much so that I often encourage new and seasoned data scientists to try it whenever they’re faced with analytics projects that have short deadlines.

I recently spent a few hours with one of scikit-learn’s core contributors Olivier Grisel. We had a free flowing discussion were we talked about machine-learning, data science, programming languages, big data, Paris, and … scikit-learn! Along the way, I was reminded by why I’ve come to use (and admire) the scikit-learn project.

Commitment to documentation and usability
One of the reasons I started2 using scikit-learn was because of its nice documentation (which I hold up as an example for other communities and projects to emulate). Contributions to scikit-learn are required to include narrative examples along with sample scripts that run on small data sets. Besides good documentation there are other core tenets that guide the community’s overall commitment to quality and usability: the global API is safeguarded, all public API’s are well documented, and when appropriate contributors are encouraged to expand the coverage of unit tests.

Models are chosen and implemented by a dedicated team of experts
scikit-learn’s stable of contributors includes experts in machine-learning and software development. A few of them (including Olivier) are able to devote a portion of their professional working hours to the project.

Covers most machine-learning tasks
Scan the list of things available in scikit-learn and you quickly realize that it includes tools for many of the standard machine-learning tasks (such as clustering, classification, regression, etc.). And since scikit-learn is developed by a large community of developers and machine-learning experts, promising new techniques tend to be included in fairly short order.

As a curated library, users don’t have to choose from multiple competing implementations of the same algorithm (a problem that R users often face). In order to assist users who struggle to choose between different models, Andreas Muller created a simple flowchart for users:

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Day-Long Immersions and Deep Dives at Strata Santa Clara 2014

Tutorials for designers, data scientists, data engineers, and managers

As the Program Development Director for Strata Santa Clara 2014, I am pleased to announce that the tutorial session descriptions are now live. We’re pleased to offer several day-long immersions including the popular Data Driven Business Day and Hardcore Data Science tracks. We curated these topics as we wanted to appeal to a broad range of attendees including business users and managers, designers, data analysts/scientists, and data engineers. In the coming months we’ll have a series of guest posts from many of the instructors and communities behind the tutorials.

Analytics for Business Users
We’re offering a series of data intensive tutorials for non-programmers. John Foreman will use spreadsheets to demonstrate how data science techniques work step-by-step – a topic that should appeal to those tasked with advanced business analysis. Grammar of Graphics author, SYSTAT creator, and noted Statistician Leland Wilkinson, will teach an introductory course on analytics using an innovative expert system he helped build.

Data Science essentials
Scalding – a Scala API for Cascading – is one of the most popular open source projects in the Hadoop ecosystem. Vitaly Gordon will lead a hands-on tutorial on how to use Scalding to put together effective data processing workflows. Data analysts have long lamented the amount of time they spend on data wrangling. But what if you had access to tools and best practices that would make data wrangling less tedious? That’s exactly the tutorial that distinguished Professors and Trifacta co-founders, Joe Hellerstein and Jeff Heer, are offering.

The co-founders of Datascope Analytics are offering a glimpse into how they help clients identify the appropriate problem or opportunity to focus on by using design thinking (see the recent Datascope/IDEO post on Design Thinking and Data Science). We’re also happy to reprise the popular (Strata Santa Clara 2013) d3.js tutorial by Scott Murray.

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