Strata Conference NYC 2013 Full Version

Taewook Eom
Data Infrastructure Team
SK planet
2014-01-28

Taewook Eom
Data Programmer
Plaster(Planet Master)
of Big Data Infra
Pre-Assessor of Hiring Programmers
Mentor of 101 Startup Korea

Twitter: @taewooke
LinkedIn: http://kr.linkedin.com/in/taewookeom
http://www.flickr.com/photos/oreillyconf/10616622085/

Santa Clara
: Technical

New York
with Cloudera

: Financial, Business

Europe

: Privacy, Government

Boston
: Medical

http://strataconf.com/

by O’Reilly
Web 2.0

: Open, Sharing, Participation

Big Data

: Making Data Work
Change the World with Data.

Data
When hardware became commoditized,
software was valuable.
Now software being commoditized,
data is valuable.
– Tim O’Reilly, 2011

Data is like the blood of the enterprise.
– Amr Awadallah, CTO at Cloudera, 2013

What is Big Data?
All data that is not a fit for a traditional RDBMS,
whether used for OLTP or Analytics purposes

Big Data Architectural Patterns
http://strataconf.com/stratany2013/public/schedule/detail/30397

Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data
- Gartner, 2011

http://blog.vitria.com/Portals/47881/images/3values-resized-600.png

http://image-store.slidesharecdn.com/ae63030a-3d9b-11e3-9cff-22000a970267-original.jpg

Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS

Data Science

http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

http://en.wikipedia.org/wiki/File:DataScienceDisciplines.png

Big Data

http://mappingignorance.org/fx/media/2013/07/Figura-11.jpg

Open Mind!

Big Data

Gartner's 2013 Hype Cycle for Emerging Technologies (2013-08-19)

more than half of
technical sessions
are presented by
Chinese or Indian

39 of 125 sessions are
sponsored sessions

Big Data: 4 Approaches
Hadoop-based

RDB-based

Search-based

NoSQL

Real-time Processing

Real-time Recommendations for Retail: Architecture, Algorithms, and Design

Real-time Stream Processing
Apache
Kafka

Gathering

Apache
Storm

Processing
Querying

Streaming
Search-based
NoSQL
SQL

Stringer/Tez

Shark

Big Data Space
No one tools is the right fit for all Big Data problem
Do not be afraid to recommend the right solution
for the problem over the popular solution
To do this, you must be aware of the entire ecosystem


Practical Performance Analysis and Tuning for Cloudera Impala

Hadoop and the Relational Data Warehouse – When to Use Which?

Each speaker is allocated five minutes of presentation time
and is accompanied by 20 presentation slides.
During presentations, each slide is displayed for 15 seconds
and then automatically advanced.
- http://en.wikipedia.org/wiki/Ignite_(event)
http://oreilly.com/pub/pr/2242

Ignite Talks
Hilary: The Most Poisoned Name In US History - Hilary Parker
sudo make me a visualization! - Jeroen Janssens
Design as a Fulcrum for Societal Change: the influence of Jimmyjane on female sexuality - Lisa Green

Spaces in Between: The Transdisciplinary Niche to Type 1 Diabetes Living - Jorge Luna

Why are women better data scientists than men? - Carolyn Martin
Memoirs of a Prolific Moonlighter: A Chronic Writing Disorder…or Insanity? - Matthew Russell
The Data Behind H1B Visas - Melissa Smolensky

Signal Detection Theory: Man vs Machine - Kyle Redinger
Algorithms of Pain - Heather Fenby
Hadoop Playlist - Adam Kawa
Why a Data Community is like a Music Scene - Harlan Harris

A Tale of two Kinds of Startups - Jen van der Meer


Ignite
Signal Detection Theory: Man vs Machine
Co-Founder @VividCortex
Kyle Redinger
http://www.youtube.com/watch?v=Fg6mN-jevds
(5 minutes 6 seconds)
http://www.slideshare.net/realkyleredinger/man-vs-machine-signal-detection-theory-and-big-data

Signal Detection Theory: Man vs Machine

Remove the obvious and look at what is important
Remember: Less is more.

Ignite
A Tale of Two Kinds of Startups
CSO at Luminary Labs
Jen van der Meer
http://www.youtube.com/watch?v=0ooIs4cy5uM
http://www.slideshare.net/bettybluegreen/twokindsofstartups

Keynote
Towards Strata 2014
Director of market research at O’Reilly Media
Roger Magoulas
http://www.youtube.com/watch?v=Ytd5VkEgQf8

http://www.oreilly.com/data/free/files/stratasurvey.pdf

Science is fundamentally about data,
but data is not fundamentally about science
Beyond R and Ph.D.s: The Mythology of Data Science Debunked
Douglas Merrill (ZestFinance)
http://www.youtube.com/watch?v=J2sgObXbIWY (8 minutes 9 seconds)

People

A data scientist is a data analyst who lives in California.

– George Roumeliotis, (Intuit)

http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html

Data
Data
Data
Data

Businessperson: Business person, Leader, Entrepreneur
Creative: Artist, Jack-of-All-Trades, Hacker
Researcher: Scientist, Researcher, Statistician
Engineer: Engineer, Developer

http://datacommunitydc.org/blog/2012/08/data-scientists-survey-results-teaser/

http://cdn.oreillystatic.com/oreilly/radarreport/0636920029014/Analyzing_the_Analyzers.pdf

Scientists think they can code,
software engineers think they are scientists.
Team them up so they collaborate.

– Scott Sorenson (Ancestry.com)

Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop

How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce

Data scientists spend their lives as data janitors
instead of leveraging their skills

– Wes McKinney (DataPad)

Building More Productive Data Science and Analytics Workflows

Keynote
Is Bigger Really Better?
Predictive Analytics
with Fine-grained Behavior Data
Professor at the NYU Stern School of Business
Foster Provost
http://www.youtube.com/watch?v=1jzMiAfLH2c

Predictive Analytics with Fine-grained Behavior Data

Predictive Analytics with Fine-grained Behavior Data

Predictive does not mean actionable.

– Scott Sorenson (Ancestry.com)


More data gives you more precision, not more prediction.
Using multiple datasets to reduce errors when measuring values.
- Ravi Iyer (Ranker.com)
Predictive Analytics with Fine-grained Understand yourData Users, and Employees
Behavior Customers,
Using Graphs of Data to

Keynote
Big Impact from Big Data
Head of Analytics at Facebook
Ken Rudin
http://www.youtube.com/watch?v=RJFwsZwTBgg

Hadoop is a hammer,
but you need other tools along with it.

Designing Your Data-Centric Organization
Josh Klahr (Pivotal)

http://www.youtube.com/watch?v=D86udfrVzrI (12 minutes)


The way you organize information
depends on the question
you intend to ask of it.

- Richard Saul Wurman
Building a Data Platform

HaDump

: Loading data into Hadoop
for not reason.

Data Science Without a Scientist


Technical people still don't understand the business needs of business people!
Business people don't know what's a table.

- Anurag Tandon (MicroStrategy)

Inject Big Data into your Corporate DNA: Enable Every Employee to Make Data Driven Decisions

Ask the Right Questions
Organizations already have people who know their own data
better than mystical data scientists.
Learning Hadoop is easier than learning the company’s business.
- Gartner, 2012

Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS

Non-linear Storytelling: Towards New Methods and Aesthetics for Data Narrative

Every Soldier is a Sensor: Countering Corruption in Afghanistan

Value of Data
Usable < Useful < Actionable
with Impact

If you can't answer for "so what?",
you only have facts, not insight
- Baron Schwartz (VividCortex Inc)
Making Big Data Small

Descriptive (Easy)
Predictive (Medium)
Prescriptive (Hard)

What happened?
What will happen?
What should we do about it?

Hadoop & Data Science for the Enterprise

The Future of Hadoop
: What Happened
& What's Possible?
Co-Founder of Hadoop
Doug Cutting
http://www.youtube.com/watch?v=_WwuZI6AhN8
http://strataconf.com/stratany2013/public/
schedule/detail/31591

Big Data is first industry that was created
by open source.

- Jack Norris (MapR Technologies)
Separating Hadoop Myths from Reality

Hadoop the kernel of the OS for data.

Hadoop's Impact on the Future of Data Management
Mike Olson (Cloudera)

http://www.youtube.com/watch?v=puHS2JNKgRM

Single
:
:
:
:
:
:

S/W & H/W system
security model
management model
metadata model
audit model
resource
management model

Common

: storage & schema
http://www.slideshare.net/cloudera/enterprise-data-hub-the-next-big-thing-in-big-data

Unifying Your Data Management Platform with Hadoop: Batch and Real-time Machine Data Ingest, Alerts, and Analytics

Last generation of data management is not sufficient
More copies, representations, transformations increase risk
Index once and reuse across workloads, lifecycle
NoSQL: indexing and updates for interactive apps
Hadoop: staging, persistence, and analytics

Data Governance for Regulated Industries Using Hadoop

Data Intelligence
Rethink How You See Data

Sharmila Shahani-Mulligan (ClearStory Data)

http://www.youtube.com/watch?v=07hGulTOZGk (9 minutes 6 seconds)

The Data Availability Problem

?

Access

Question
Sampling

Analysis & Disc
Modeling
overy

Loading
Insight

Data Prep – too slow!

Information Supply Chain
Introducing a New Way to Interact with Insight

Presentation

Running Non-MapReduce Big Data applications on Apache Hadoop

Apache HBase for Architects
What’s Next for Apache HBase: Multi-tenancy, Predictability, and Extensions.

Securing the Apache Hadoop Ecosystem

An Introduction to the Berkeley Data Analytics Stack With Spark, Spark Streaming, Shark, Tachyon, and BlinkDB

Schema
Information does not exist until a schema is defined
and data is stored in a relational database

- anonymous

Building a Data Platform

Lessons Learned From A Decade’s Worth of Big Data At The U.S. National Security Agency (NSA)

Managing a Rapidly Evolving Analytics Pipeline

Stringer/Tez

Shark

SQL on/in Hadoop/Hbase Solutions

Perception is Key: Telescopes, Microscopes and Data
http://strataconf.com/strataeu2013/public/schedule/detail/32351

All SQL on Hadoop Solutions are
Missing the Point of Hadoop
Every Solution makes you define a schema

- SQL(Structured Query Language) is expressed over an assumed schema

Major reasons why Hadoop has taken of include:

- Ability to load data without defining a schema
- Process data using schema-on-read instead of first defining a schema

Hadoop contains a lot of:

- Raw, granular data sets with potentially inconsistent schemas
- Data sets in JSON, key-value, and other self-describing (non-relational) models
designed for schema-on-read processing

SQL on Hadoop solutions that make you first define a schema are missing
a major part of Hadoop’s usage patterns

Flexible Schema and the End of ETL

Hadoop Adventures At Spotify

Quick prototyping is the fastest way to internal advocacy. Ship It!
Cloud == Speed
We don’t always need a complicated solution. KISS
Play to your differentiating strengths. Experience >> Data
Bias towards impact.
It Takes a Village
EASE!! (Emulate, Analyze, Scale, Evaluate)

Prototyping is key to overcoming resistance to change
Technical architecture is heavily influenced by people organization
Developing a team of experienced Hadoop users can often be done
using internal employees
A culture of experimentation and innovation yields the best result

Questions?
SELECT questions FROM audience;

References
Strata Conference + Hadoop World 2013 Keynotes & Interviews

http://www.youtube.com/playlist?list=PL055Epbe6d5ZtziVAooUC04i1hL_Z9Xvk

Slides & Video

http://strataconf.com/stratany2013/public/schedule/proceedings

Tweets

https://twitter.com/search?q=%23strataconf #strataconf

http://nordstrom.github.io/stratanyc/

http://complexdiagrams.com/properties
Four Pillars of Visualization

Building a production machine learning infrastructure
http://www.slideshare.net/joshwills/production-machine-learninginfrastructure

Text Analytics at Scale: Listening to 45 Million Customers

Words + Numbers = Insights

Text Analytics at Scale: Listening to 45 Million Customers

Strata Conference NYC 2013 Full Version

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