SlideShare a Scribd company logo
1 of 76
Download to read offline
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
http://strataconf.com/stratany2013/public/schedule/detail/29968
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
http://strataconf.com/stratany2013/public/schedule/detail/30217
Real-time Stream Processing
Apache
Kafka

Gathering

Apache
Storm

Processing
Querying

Streaming
Search-based
NoSQL
SQL

Stringer/Tez

Shark
… not yet Graph Processing
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

Big Data Architectural Patterns
http://strataconf.com/stratany2013/public/schedule/detail/30397
Practical Performance Analysis and Tuning for Cloudera Impala
http://strataconf.com/stratany2013/public/schedule/detail/30551
Hadoop and the Relational Data Warehouse – When to Use Which?
http://strataconf.com/stratany2013/public/schedule/detail/30964
Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS
http://strataconf.com/stratany2013/public/schedule/detail/29968
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.
Keynote
Towards Strata 2014
Director of market research at O’Reilly Media
Roger Magoulas
http://www.youtube.com/watch?v=Ytd5VkEgQf8
(5 minutes 26 seconds)
http://strataconf.com/stratany2013/public/schedule/detail/31935

http://www.oreilly.com/data/free/files/stratasurvey.pdf
Towards Strata 2014
Towards Strata 2014
Towards Strata 2014
Towards Strata 2014
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
http://strataconf.com/stratany2013/public/schedule/detail/30707
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
(10 minutes 16 seconds)
http://strataconf.com/stratany2013/public/schedule/detail/31685
Is Bigger Really Better?
Predictive Analytics with Fine-grained Behavior Data
Is Bigger Really Better?
Predictive Analytics with Fine-grained Behavior Data
Is Bigger Really Better?
Predictive Analytics with Fine-grained Behavior Data

Predictive does not mean actionable.

– Scott Sorenson (Ancestry.com)

Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop
More data gives you more precision, not more prediction.
Using multiple datasets to reduce errors when measuring values.
Is Bigger Really Better?
- Ravi Iyer (Ranker.com)
Predictive Analytics with Fine-grained Understand yourData Users, and Employees
Behavior Customers,
Using Graphs of Data to
Is Bigger Really Better?
Predictive Analytics with Fine-grained Behavior Data
Is Bigger Really Better?
Predictive Analytics with Fine-grained Behavior Data
Keynote
Big Impact from Big Data
Head of Analytics at Facebook
Ken Rudin
http://www.youtube.com/watch?v=RJFwsZwTBgg
(11 minutes 57 seconds)
http://strataconf.com/stratany2013/public/schedule/detail/31903
Big Impact from Big Data
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)
Big Impact from Big Data

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
http://strataconf.com/stratany2013/public/schedule/detail/31801
Big Impact from Big Data

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
http://strataconf.com/stratany2013/public/schedule/detail/29968
Non-linear Storytelling: Towards New Methods and Aesthetics for Data Narrative
http://strataconf.com/stratany2013/public/schedule/detail/30207
Every Soldier is a Sensor: Countering Corruption in Afghanistan
http://strataconf.com/stratany2013/public/schedule/detail/30828
Big Impact from Big Data
Big Impact from Big Data
Big Impact from Big Data
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
(14 minutes 41 seconds)
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
http://strataconf.com/stratany2013/public/schedule/detail/31380
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
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
http://strataconf.com/stratany2013/public/schedule/detail/30738
Data Intelligence
Rethink How You See Data

Sharmila Shahani-Mulligan (ClearStory Data)

http://www.youtube.com/watch?v=07hGulTOZGk (9 minutes 6 seconds)
http://strataconf.com/stratany2013/public/schedule/detail/31742
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
http://strataconf.com/stratany2013/public/schedule/detail/31743

Presentation
Running Non-MapReduce Big Data applications on Apache Hadoop
http://strataconf.com/stratany2013/public/schedule/detail/30755
Apache HBase for Architects
http://strataconf.com/stratany2013/public/schedule/detail/30619
What’s Next for Apache HBase: Multi-tenancy, Predictability, and Extensions.
http://strataconf.com/stratany2013/public/schedule/detail/30857
Securing the Apache Hadoop Ecosystem
http://strataconf.com/stratany2013/public/schedule/detail/30302
An Introduction to the Berkeley Data Analytics Stack With Spark, Spark Streaming, Shark, Tachyon, and BlinkDB
http://strataconf.com/stratany2013/public/schedule/detail/30959
Schema
Information does not exist until a schema is defined
and data is stored in a relational database

- anonymous

Building a Data Platform
http://strataconf.com/stratany2013/public/schedule/detail/31400
Lessons Learned From A Decade’s Worth of Big Data At The U.S. National Security Agency (NSA)
http://strataconf.com/stratany2013/public/schedule/detail/30913
Managing a Rapidly Evolving Analytics Pipeline
http://strataconf.com/stratany2013/public/schedule/detail/30635
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
http://strataconf.com/stratany2013/public/schedule/detail/31868
Lessons Learned
Hadoop Adventures At Spotify
http://strataconf.com/stratany2013/public/schedule/detail/30570
Hadoop Adventures At Spotify
http://strataconf.com/stratany2013/public/schedule/detail/30570
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)
How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce
http://strataconf.com/stratany2013/public/schedule/detail/30707

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
Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop
http://strataconf.com/stratany2013/public/schedule/detail/30499
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

More Related Content

What's hot

Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data ScienceLivePerson
 
Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations Domino Data Lab
 
Data and information
Data and informationData and information
Data and informationsteveathon
 
Data Analaytics.04. Data visualization
Data Analaytics.04. Data visualizationData Analaytics.04. Data visualization
Data Analaytics.04. Data visualizationAlex Rayón Jerez
 
What's the Value of Data Science for Organizations: Tips for Invincibility in...
What's the Value of Data Science for Organizations: Tips for Invincibility in...What's the Value of Data Science for Organizations: Tips for Invincibility in...
What's the Value of Data Science for Organizations: Tips for Invincibility in...Ganes Kesari
 
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-Shapiro
Keynote -  An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroKeynote -  An overview on Big Data & Data Science - Dr Gregory Piatetsky-Shapiro
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroData ScienceTech Institute
 
Less is More: Behind the Data at Risk I/O
Less is More: Behind the Data at Risk I/OLess is More: Behind the Data at Risk I/O
Less is More: Behind the Data at Risk I/OMichael Roytman
 

What's hot (9)

Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations Data Science Popup Austin: Conflict in Growing Data Science Organizations
Data Science Popup Austin: Conflict in Growing Data Science Organizations
 
Analytics Education in the era of Big Data
Analytics Education in the era of Big DataAnalytics Education in the era of Big Data
Analytics Education in the era of Big Data
 
Data and information
Data and informationData and information
Data and information
 
Data science and_analytics_for_ordinary_people_ebook
Data science and_analytics_for_ordinary_people_ebookData science and_analytics_for_ordinary_people_ebook
Data science and_analytics_for_ordinary_people_ebook
 
Data Analaytics.04. Data visualization
Data Analaytics.04. Data visualizationData Analaytics.04. Data visualization
Data Analaytics.04. Data visualization
 
What's the Value of Data Science for Organizations: Tips for Invincibility in...
What's the Value of Data Science for Organizations: Tips for Invincibility in...What's the Value of Data Science for Organizations: Tips for Invincibility in...
What's the Value of Data Science for Organizations: Tips for Invincibility in...
 
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-Shapiro
Keynote -  An overview on Big Data & Data Science - Dr Gregory Piatetsky-ShapiroKeynote -  An overview on Big Data & Data Science - Dr Gregory Piatetsky-Shapiro
Keynote - An overview on Big Data & Data Science - Dr Gregory Piatetsky-Shapiro
 
Less is More: Behind the Data at Risk I/O
Less is More: Behind the Data at Risk I/OLess is More: Behind the Data at Risk I/O
Less is More: Behind the Data at Risk I/O
 

Viewers also liked

Strata Conference NYC 2013 Full Version
Strata Conference NYC 2013 Full VersionStrata Conference NYC 2013 Full Version
Strata Conference NYC 2013 Full VersionTaewook Eom
 
TOC 2011: Content as Application, presented by Scott Grillo
TOC 2011: Content as Application, presented by Scott GrilloTOC 2011: Content as Application, presented by Scott Grillo
TOC 2011: Content as Application, presented by Scott GrilloSilverchair
 
TOC 2011: Content as Application, presented by Thane Kerner
TOC 2011: Content as Application, presented by Thane KernerTOC 2011: Content as Application, presented by Thane Kerner
TOC 2011: Content as Application, presented by Thane KernerSilverchair
 
Extreme Web Performance for Mobile Devices
Extreme Web Performance for Mobile Devices Extreme Web Performance for Mobile Devices
Extreme Web Performance for Mobile Devices Maximiliano Firtman
 
Costa Pacifica in Baler, Aurora
Costa Pacifica in Baler, AuroraCosta Pacifica in Baler, Aurora
Costa Pacifica in Baler, AuroraClaire Algarme
 
Mobile & Desktop Cache 2.0: How To Create A Scriptable Cache
Mobile & Desktop Cache 2.0: How To Create A Scriptable CacheMobile & Desktop Cache 2.0: How To Create A Scriptable Cache
Mobile & Desktop Cache 2.0: How To Create A Scriptable CacheBlaze Software Inc.
 
Continuous Delivery in Financial Trading at IG
Continuous Delivery in Financial Trading at IGContinuous Delivery in Financial Trading at IG
Continuous Delivery in Financial Trading at IGDavid Genn
 
Velocity 2015-tim-prendergast-continuous-security-the-devops-way
Velocity 2015-tim-prendergast-continuous-security-the-devops-wayVelocity 2015-tim-prendergast-continuous-security-the-devops-way
Velocity 2015-tim-prendergast-continuous-security-the-devops-wayEvident.io
 
Can you wireframe 'Delightful'?
Can you wireframe 'Delightful'?Can you wireframe 'Delightful'?
Can you wireframe 'Delightful'?Ben Tollady
 
Is there such a thing as a good business model for publishing these days?
Is there such a thing as a good business model  for publishing these days?Is there such a thing as a good business model  for publishing these days?
Is there such a thing as a good business model for publishing these days?Louis Rosenfeld
 
Forensic Tools for In-Depth Performance Investigations
Forensic Tools for In-Depth Performance InvestigationsForensic Tools for In-Depth Performance Investigations
Forensic Tools for In-Depth Performance InvestigationsNicholas Jansma
 
We Are Killing Serendipity
We Are Killing SerendipityWe Are Killing Serendipity
We Are Killing SerendipitySchneider, Mike
 
Locked Out in London (and tweeting about it)
Locked Out in London (and tweeting about it)Locked Out in London (and tweeting about it)
Locked Out in London (and tweeting about it)Sylvain Carle
 
What You Need to Know About Email Authentication
What You Need to Know About Email AuthenticationWhat You Need to Know About Email Authentication
What You Need to Know About Email AuthenticationKurt Andersen
 
TOC 2011: Content as Application, presented by Reid Sherline
TOC 2011: Content as Application, presented by Reid SherlineTOC 2011: Content as Application, presented by Reid Sherline
TOC 2011: Content as Application, presented by Reid SherlineSilverchair
 
Case Studies: Harnessing Speed for Competitive Advantage
Case Studies: Harnessing Speed for Competitive AdvantageCase Studies: Harnessing Speed for Competitive Advantage
Case Studies: Harnessing Speed for Competitive AdvantageVMware Tanzu
 
Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Guy Harrison
 
Branding Presentation Robin Horne Casa Pacifica
Branding Presentation Robin Horne Casa PacificaBranding Presentation Robin Horne Casa Pacifica
Branding Presentation Robin Horne Casa PacificaRobin Horne
 

Viewers also liked (20)

Strata Conference NYC 2013 Full Version
Strata Conference NYC 2013 Full VersionStrata Conference NYC 2013 Full Version
Strata Conference NYC 2013 Full Version
 
TOC 2011: Content as Application, presented by Scott Grillo
TOC 2011: Content as Application, presented by Scott GrilloTOC 2011: Content as Application, presented by Scott Grillo
TOC 2011: Content as Application, presented by Scott Grillo
 
TOC 2011: Content as Application, presented by Thane Kerner
TOC 2011: Content as Application, presented by Thane KernerTOC 2011: Content as Application, presented by Thane Kerner
TOC 2011: Content as Application, presented by Thane Kerner
 
Extreme Web Performance for Mobile Devices
Extreme Web Performance for Mobile Devices Extreme Web Performance for Mobile Devices
Extreme Web Performance for Mobile Devices
 
Costa Pacifica in Baler, Aurora
Costa Pacifica in Baler, AuroraCosta Pacifica in Baler, Aurora
Costa Pacifica in Baler, Aurora
 
Mobile & Desktop Cache 2.0: How To Create A Scriptable Cache
Mobile & Desktop Cache 2.0: How To Create A Scriptable CacheMobile & Desktop Cache 2.0: How To Create A Scriptable Cache
Mobile & Desktop Cache 2.0: How To Create A Scriptable Cache
 
Pacifica Affiliates Program
Pacifica Affiliates ProgramPacifica Affiliates Program
Pacifica Affiliates Program
 
Continuous Delivery in Financial Trading at IG
Continuous Delivery in Financial Trading at IGContinuous Delivery in Financial Trading at IG
Continuous Delivery in Financial Trading at IG
 
Velocity 2015-tim-prendergast-continuous-security-the-devops-way
Velocity 2015-tim-prendergast-continuous-security-the-devops-wayVelocity 2015-tim-prendergast-continuous-security-the-devops-way
Velocity 2015-tim-prendergast-continuous-security-the-devops-way
 
Can you wireframe 'Delightful'?
Can you wireframe 'Delightful'?Can you wireframe 'Delightful'?
Can you wireframe 'Delightful'?
 
Is there such a thing as a good business model for publishing these days?
Is there such a thing as a good business model  for publishing these days?Is there such a thing as a good business model  for publishing these days?
Is there such a thing as a good business model for publishing these days?
 
Forensic Tools for In-Depth Performance Investigations
Forensic Tools for In-Depth Performance InvestigationsForensic Tools for In-Depth Performance Investigations
Forensic Tools for In-Depth Performance Investigations
 
We Are Killing Serendipity
We Are Killing SerendipityWe Are Killing Serendipity
We Are Killing Serendipity
 
Locked Out in London (and tweeting about it)
Locked Out in London (and tweeting about it)Locked Out in London (and tweeting about it)
Locked Out in London (and tweeting about it)
 
What You Need to Know About Email Authentication
What You Need to Know About Email AuthenticationWhat You Need to Know About Email Authentication
What You Need to Know About Email Authentication
 
TOC 2011: Content as Application, presented by Reid Sherline
TOC 2011: Content as Application, presented by Reid SherlineTOC 2011: Content as Application, presented by Reid Sherline
TOC 2011: Content as Application, presented by Reid Sherline
 
Case Studies: Harnessing Speed for Competitive Advantage
Case Studies: Harnessing Speed for Competitive AdvantageCase Studies: Harnessing Speed for Competitive Advantage
Case Studies: Harnessing Speed for Competitive Advantage
 
Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop Hadoop and rdbms with sqoop
Hadoop and rdbms with sqoop
 
Advanced Sqoop
Advanced Sqoop Advanced Sqoop
Advanced Sqoop
 
Branding Presentation Robin Horne Casa Pacifica
Branding Presentation Robin Horne Casa PacificaBranding Presentation Robin Horne Casa Pacifica
Branding Presentation Robin Horne Casa Pacifica
 

Similar to Towards the Future of Big Data

Making friends with big data resource links
Making friends with big data resource linksMaking friends with big data resource links
Making friends with big data resource linksHeather Stark
 
Business intelligence 3.0 and the data lake
Business intelligence 3.0 and the data lakeBusiness intelligence 3.0 and the data lake
Business intelligence 3.0 and the data lakeData Science Thailand
 
AI in Business - Key drivers and future value
AI in Business - Key drivers and future valueAI in Business - Key drivers and future value
AI in Business - Key drivers and future valueAPPANION
 
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALSecrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALMark Tabladillo
 
Come diventare data scientist - Paolo Pellegrini
Come diventare data scientist - Paolo PellegriniCome diventare data scientist - Paolo Pellegrini
Come diventare data scientist - Paolo PellegriniDonatella Cambosu
 
Module 6 The Future of Big and Smart Data- Online
Module 6 The Future of Big and Smart Data- Online Module 6 The Future of Big and Smart Data- Online
Module 6 The Future of Big and Smart Data- Online caniceconsulting
 
SQL PASS BA London 2014 - Data Culture & Future of Analytics
SQL PASS BA London 2014 - Data Culture & Future of AnalyticsSQL PASS BA London 2014 - Data Culture & Future of Analytics
SQL PASS BA London 2014 - Data Culture & Future of AnalyticsJonathan Woodward
 
Hadoop for beginners free course ppt
Hadoop for beginners   free course pptHadoop for beginners   free course ppt
Hadoop for beginners free course pptNjain85
 
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptxarpit206900
 
Scalable Predictive Analysis and The Trend with Big Data & AI
Scalable Predictive Analysis and The Trend with Big Data & AIScalable Predictive Analysis and The Trend with Big Data & AI
Scalable Predictive Analysis and The Trend with Big Data & AIJongwook Woo
 
BigData Meets the Federal Data Center
BigData Meets the Federal Data CenterBigData Meets the Federal Data Center
BigData Meets the Federal Data CenterAbe Usher
 
From Lab to Factory: Creating value with data
From Lab to Factory: Creating value with dataFrom Lab to Factory: Creating value with data
From Lab to Factory: Creating value with dataPeadar Coyle
 
Collaborative Data UX Design - Virtually and Phyically
Collaborative Data UX Design - Virtually and Phyically Collaborative Data UX Design - Virtually and Phyically
Collaborative Data UX Design - Virtually and Phyically Datentreiber
 
Data set The Future of Big Data
Data set The Future of Big DataData set The Future of Big Data
Data set The Future of Big DataData-Set
 
Data Scientists: Your Must-Have Business Investment
Data Scientists: Your Must-Have Business InvestmentData Scientists: Your Must-Have Business Investment
Data Scientists: Your Must-Have Business InvestmentKalido
 

Similar to Towards the Future of Big Data (20)

Making friends with big data resource links
Making friends with big data resource linksMaking friends with big data resource links
Making friends with big data resource links
 
Business intelligence 3.0 and the data lake
Business intelligence 3.0 and the data lakeBusiness intelligence 3.0 and the data lake
Business intelligence 3.0 and the data lake
 
Big Data - Where from Where to
Big Data - Where from Where toBig Data - Where from Where to
Big Data - Where from Where to
 
AI in Business - Key drivers and future value
AI in Business - Key drivers and future valueAI in Business - Key drivers and future value
AI in Business - Key drivers and future value
 
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham ALSecrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
Secrets of Enterprise Data Mining: SQL Saturday 328 Birmingham AL
 
Come diventare data scientist - Paolo Pellegrini
Come diventare data scientist - Paolo PellegriniCome diventare data scientist - Paolo Pellegrini
Come diventare data scientist - Paolo Pellegrini
 
10 Keynotes in STRATA and HADOOP World Conference
10 Keynotes in STRATA and HADOOP World Conference10 Keynotes in STRATA and HADOOP World Conference
10 Keynotes in STRATA and HADOOP World Conference
 
Module 6 The Future of Big and Smart Data- Online
Module 6 The Future of Big and Smart Data- Online Module 6 The Future of Big and Smart Data- Online
Module 6 The Future of Big and Smart Data- Online
 
Data scienceppt
Data sciencepptData scienceppt
Data scienceppt
 
SQL PASS BA London 2014 - Data Culture & Future of Analytics
SQL PASS BA London 2014 - Data Culture & Future of AnalyticsSQL PASS BA London 2014 - Data Culture & Future of Analytics
SQL PASS BA London 2014 - Data Culture & Future of Analytics
 
Hadoop for beginners free course ppt
Hadoop for beginners   free course pptHadoop for beginners   free course ppt
Hadoop for beginners free course ppt
 
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx
 
Scalable Predictive Analysis and The Trend with Big Data & AI
Scalable Predictive Analysis and The Trend with Big Data & AIScalable Predictive Analysis and The Trend with Big Data & AI
Scalable Predictive Analysis and The Trend with Big Data & AI
 
Complete-SRS.doc
Complete-SRS.docComplete-SRS.doc
Complete-SRS.doc
 
BigData Meets the Federal Data Center
BigData Meets the Federal Data CenterBigData Meets the Federal Data Center
BigData Meets the Federal Data Center
 
From Lab to Factory: Creating value with data
From Lab to Factory: Creating value with dataFrom Lab to Factory: Creating value with data
From Lab to Factory: Creating value with data
 
Data Driven Economy @CMU
Data Driven Economy @CMUData Driven Economy @CMU
Data Driven Economy @CMU
 
Collaborative Data UX Design - Virtually and Phyically
Collaborative Data UX Design - Virtually and Phyically Collaborative Data UX Design - Virtually and Phyically
Collaborative Data UX Design - Virtually and Phyically
 
Data set The Future of Big Data
Data set The Future of Big DataData set The Future of Big Data
Data set The Future of Big Data
 
Data Scientists: Your Must-Have Business Investment
Data Scientists: Your Must-Have Business InvestmentData Scientists: Your Must-Have Business Investment
Data Scientists: Your Must-Have Business Investment
 

Recently uploaded

DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DaySri Ambati
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupFlorian Wilhelm
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 

Recently uploaded (20)

DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo DayH2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
H2O.ai CEO/Founder: Sri Ambati Keynote at Wells Fargo Day
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project SetupStreamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 

Towards the Future of Big Data

  • 1. Taewook Eom Data Infrastructure Team SK planet 2014-01-28
  • 2. 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/
  • 3. 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.
  • 4. 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
  • 5. 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
  • 6. 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
  • 8. Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS http://strataconf.com/stratany2013/public/schedule/detail/29968
  • 11. Big Data Gartner's 2013 Hype Cycle for Emerging Technologies (2013-08-19)
  • 12. more than half of technical sessions are presented by Chinese or Indian 39 of 125 sessions are sponsored sessions
  • 13. Big Data: 4 Approaches Hadoop-based RDB-based Search-based NoSQL
  • 14. Real-time Processing Real-time Recommendations for Retail: Architecture, Algorithms, and Design http://strataconf.com/stratany2013/public/schedule/detail/30217
  • 16. … not yet Graph Processing
  • 17. 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 Big Data Architectural Patterns http://strataconf.com/stratany2013/public/schedule/detail/30397
  • 18. Practical Performance Analysis and Tuning for Cloudera Impala http://strataconf.com/stratany2013/public/schedule/detail/30551
  • 19. Hadoop and the Relational Data Warehouse – When to Use Which? http://strataconf.com/stratany2013/public/schedule/detail/30964
  • 20. Defining your Big Data Arsenal: NoSQL, Hadoop, and RDBMS http://strataconf.com/stratany2013/public/schedule/detail/29968
  • 21. 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
  • 22. Signal Detection Theory: Man vs Machine Remove the obvious and look at what is important Remember: Less is more.
  • 23. Keynote Towards Strata 2014 Director of market research at O’Reilly Media Roger Magoulas http://www.youtube.com/watch?v=Ytd5VkEgQf8 (5 minutes 26 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31935 http://www.oreilly.com/data/free/files/stratasurvey.pdf
  • 28. 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)
  • 29. People A data scientist is a data analyst who lives in California. – George Roumeliotis, (Intuit)
  • 31. 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
  • 32. 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
  • 33. How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce http://strataconf.com/stratany2013/public/schedule/detail/30707
  • 34. Data scientists spend their lives as data janitors instead of leveraging their skills – Wes McKinney (DataPad) Building More Productive Data Science and Analytics Workflows
  • 35. 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 (10 minutes 16 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31685
  • 36. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  • 37. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  • 38. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data Predictive does not mean actionable. – Scott Sorenson (Ancestry.com) Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop
  • 39. More data gives you more precision, not more prediction. Using multiple datasets to reduce errors when measuring values. Is Bigger Really Better? - Ravi Iyer (Ranker.com) Predictive Analytics with Fine-grained Understand yourData Users, and Employees Behavior Customers, Using Graphs of Data to
  • 40. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  • 41. Is Bigger Really Better? Predictive Analytics with Fine-grained Behavior Data
  • 42. Keynote Big Impact from Big Data Head of Analytics at Facebook Ken Rudin http://www.youtube.com/watch?v=RJFwsZwTBgg (11 minutes 57 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31903
  • 43. Big Impact from Big Data
  • 44. 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)
  • 45. Big Impact from Big Data The way you organize information depends on the question you intend to ask of it. - Richard Saul Wurman Building a Data Platform
  • 46. HaDump : Loading data into Hadoop for not reason. Data Science Without a Scientist http://strataconf.com/stratany2013/public/schedule/detail/31801
  • 47. Big Impact from Big Data 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
  • 48. 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 http://strataconf.com/stratany2013/public/schedule/detail/29968
  • 49. Non-linear Storytelling: Towards New Methods and Aesthetics for Data Narrative http://strataconf.com/stratany2013/public/schedule/detail/30207
  • 50. Every Soldier is a Sensor: Countering Corruption in Afghanistan http://strataconf.com/stratany2013/public/schedule/detail/30828
  • 51. Big Impact from Big Data
  • 52. Big Impact from Big Data
  • 53. Big Impact from Big Data
  • 54. 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
  • 55. The Future of Hadoop : What Happened & What's Possible? Co-Founder of Hadoop Doug Cutting http://www.youtube.com/watch?v=_WwuZI6AhN8 (14 minutes 41 seconds) 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.
  • 56. Hadoop's Impact on the Future of Data Management Mike Olson (Cloudera) http://www.youtube.com/watch?v=puHS2JNKgRM http://strataconf.com/stratany2013/public/schedule/detail/31380
  • 57. 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
  • 58. 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 http://strataconf.com/stratany2013/public/schedule/detail/30738
  • 59. Data Intelligence Rethink How You See Data Sharmila Shahani-Mulligan (ClearStory Data) http://www.youtube.com/watch?v=07hGulTOZGk (9 minutes 6 seconds) http://strataconf.com/stratany2013/public/schedule/detail/31742
  • 60. 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 http://strataconf.com/stratany2013/public/schedule/detail/31743 Presentation
  • 61. Running Non-MapReduce Big Data applications on Apache Hadoop http://strataconf.com/stratany2013/public/schedule/detail/30755
  • 62. Apache HBase for Architects http://strataconf.com/stratany2013/public/schedule/detail/30619 What’s Next for Apache HBase: Multi-tenancy, Predictability, and Extensions. http://strataconf.com/stratany2013/public/schedule/detail/30857
  • 63. Securing the Apache Hadoop Ecosystem http://strataconf.com/stratany2013/public/schedule/detail/30302
  • 64. An Introduction to the Berkeley Data Analytics Stack With Spark, Spark Streaming, Shark, Tachyon, and BlinkDB http://strataconf.com/stratany2013/public/schedule/detail/30959
  • 65. Schema Information does not exist until a schema is defined and data is stored in a relational database - anonymous Building a Data Platform http://strataconf.com/stratany2013/public/schedule/detail/31400
  • 66. Lessons Learned From A Decade’s Worth of Big Data At The U.S. National Security Agency (NSA) http://strataconf.com/stratany2013/public/schedule/detail/30913
  • 67. Managing a Rapidly Evolving Analytics Pipeline http://strataconf.com/stratany2013/public/schedule/detail/30635
  • 68. Stringer/Tez Shark SQL on/in Hadoop/Hbase Solutions Perception is Key: Telescopes, Microscopes and Data http://strataconf.com/strataeu2013/public/schedule/detail/32351
  • 69. 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 http://strataconf.com/stratany2013/public/schedule/detail/31868
  • 71. Hadoop Adventures At Spotify http://strataconf.com/stratany2013/public/schedule/detail/30570
  • 72. Hadoop Adventures At Spotify http://strataconf.com/stratany2013/public/schedule/detail/30570
  • 73. 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) How Nordstrom Utilizes Human Intelligence to Blend Brick-and-Mortar with Online Commerce http://strataconf.com/stratany2013/public/schedule/detail/30707 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 Ancestry.com: Managing Big Data Reaching Back to the 11th Century with Hadoop http://strataconf.com/stratany2013/public/schedule/detail/30499
  • 74.
  • 76. 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