Cloud Computing ...changes everything

Spark has established itself as the most popular platform for advanced scale-out analytical applications. It is deeply integrated with the Hadoop ecosystem, offers a set of powerful libraries and supports both Python and R. Because of these reasons Data Scientists have started to adopt Spark to train and deploy their models. When Spark 1.4 was released back in 2015, it included the new SparkR library: this API gave R users the exciting new option to run R code on Spark. And while the initial promise to provide a full R environment in Spark has been kept, it takes a deeper understanding of SparkR’s inner workings to make optimal use of its capabilities. This talk will give a comprehensive update on where we stand with Data Science applications in R based on the latest Spark releases. We will share insights from both a Startup solution and a Fortune 100 company where SparkR does Machine Learning in the Cloud on a scale that would have not been feasible previously: it’s parallel execution model runs in minutes and hours whereas conventional sequential approaches would take days and months. Suggested Topics: • An update on the SparkR architecture in the latest Spark release: using R with SparkSQL, MLlib and Spark’s Structured Streaming • How to handle practical challenges, e.g. running R on the cluster without a local installation, storing non-tabular results, such as Data Science models or plots, mixing Scala and R. • Scaling Big Compute Applications with SparkR: Parallelizing SparkR applications with User-Defined Functions (UDFs) and elastic scaling of resources in the Cloud • An Outlook on Machine Learning with SparkR and its ecosystem, frameworks and tools. • Plus: “Do I need to learn Python?”

Presented at SDForum. October 2009

Technology

[object Object],[object Object],[object Object],[object Object],Cloud Computing ... changes everything

Cloud Computing.... Virtualization Grid computing Application hosting Utility computing Platform as a service Infrastructure as a service Software as a service

Some definitions ,[object Object],[object Object],[object Object],[object Object]

Analogy with the evolution of electrical power From each company generating their own power to a utility

When and will this happen? 2000 2005 2010? 2015? 2020? 2025? Utility / Cloud Computing Private DataCenters

Clouds reach the tipping point – AWS Bandwidth exceeds Amazon.com

Growth of massive amounts of data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

History lesson: 1987 Connection Machine 65,536 processors and lots of blinking lights

Truly Massive Scale TACC Sun Magnum 3456 Switch TACC Supercomputer center

Putting it all together – TACC Specifications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Data Center Level Computing Google’s new data center on the Columbia river, Oregon Thousands and thousands of commodity parts built into a system to essentially serve a single application Power and Cooling major drivers of cost

Data + compute -> new opportunities 'Semi-structured' data emerges Mogile, Bigtable, Hypertable ... New 'Analytics' emerge MapReduce, Hadoop ... New 'Compute' New 'Data'

How do the new guys compete? Web server app server database server Storage system Web server Web server app server database server Storage system Load balancer distributed memory cache

Economics Driving Utility Computing ,[object Object],[object Object],[object Object],[object Object],“ Let me be very clear here: I really don’t want to operate datacenters anymore... We’d rather spend our time giving our customers great service and writing great software rather than managing physical hardware,” Don MacAskill, CEO, Smugmug

What changes for developers? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Hardware Virtualization ,[object Object],[object Object],Compute Node Compute Node Compute Node Storage Node Storage Node Network

Patterns emerge in architecture - learn from others

Master-worker, queue-based design pattern - simple, dynamic scaling ,[object Object],App Server Master Worker N Worker 3 Worker 1 Worker 2 Message Queue .... Internet

Automation – scaling from 50 to 3500 servers in 3 days 4/11 4/12 4/13 4/13 4/14 4/15 4/15 4/16 4/17 4/17 4/18 4000 3000 2000 1500 1000 500 50 Animoto case study Servers

Map Reduce design pattern Documents Map <word, cnt> Map <word, cnt> Map <word, cnt> hash(word, mod R)‏ Reduce <word, cnt> Reduce <word, cnt> Reduce <word, cnt> Result <apple, 2,045,455> <barn, 254,345> Example: compute word frequency over a large set of documents Map <word, cnt> Result <house, 111,341> <kitchen, 4,678> Result <cat, 1,033,746> <horse, 25,387> Reduce workers: reads and sorts Map output, invoking Reduce() task which sums counts for each word Map workers: reads input, invoking Map() function which finds words and outputs records for reduce workers P 1 P 1 P 1 P 1

We are all part of the co-generation of information and information processing ,[object Object],[object Object]

Questions to ask yourself ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Thank you Lew Tucker Sun Microsystems, Inc

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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™: See how to accelerate model training and optimize model performance with active learning Learn about the latest enhancements to out-of-the-box document processing – with little to no training required Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath. Speakers: 👨‍🏫 Andras Palfi, Senior Product Manager, UiPath 👩‍🏫 Lenka Dulovicova, Product Program Manager, UiPath

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