SlideShare a Scribd company logo
1 of 10
Download to read offline
Panel Discussions



James Chen (Executive Officer/Vice
Managing Director of Development Unit/Manager
of Rakuten Ichiba Service Development &
Operation Department, Rakuten, Inc.)
Terje Marthinussen (Executive Officer /
Vice Managing Director of Development Unit /
Chief Architect of Next Generation Search,
Rakuten, Inc.)
Hirotaka Yoshioka (Technical Managing
Officer, Rakuten Inc.)



                                                1
James Chen

                     Title: Executive Officer
                     Mission: To build the Rakuten global
                     ecommerce platform and ecosystem
                     – Leverage the best skills and background from
                       around the world
   mitnerd           – Leverage the latest technology trends (PaaS,
                       100% API based systems)
twitter @mitnerd     – Build out a globally distributed development
                       process/teams.
http://www.mitnerd
.com/                Joined Rakuten since September 2009,
                     CTO of multiple internet service related
                     companies. Both startups and large
                     companies
                     Still enjoys hands-on coding!!!
                                                                      2
Terje Marthinussen

        Title: Executive Office/Search Architect
        Mission: Improve search and other large
        scale data processing problems.
        – Development, scaling, operations
        Joined Rakuten February 2010 (but
Terje   worked with Rakuten since 2003)
        11 years of large scale search experience.
        (Fast Search & Microsoft)
        19 years in large scale IT environments
        Worked in 4 countries on 3 continents



                                                     3
Hiro Yoshioka

                             Title: Technical Managing Officer
                             Mission: Lead the technical direction of
                             Rakuten
                             – Improve software development productivity
                               (introducing Agile software development).
     Yoshioka                – Build Scalable computer
                             – Build developers community in Rakuten and
twitter @hyoshiok              promote OSS strategy
                             Joined Rakuten since August 2009,
                             before it, he was a founding member
                             and CTO of Miracle Linux since 2000.
                             More than 25 years experience in the IT.
                             OSS activist
DEBUG HACKS (in Japanese),
O’reilly Japan                                                             4
Agile
Big Data
Globalization




                5
Big Data – Not a linear growth problem


Gartner: 3 dimensional problem:
– Volume
   • 13 Exabyte global data growth expected in 2010
   • Annual growth 70%.

– Variety
   • Complexity and types and sources

– Velocity
   • Speed/frequency of data access
   • Cisco: Avg. annual Internet traffic +32% next 5 years



                                                             6
Rakuten Ichiba – Multi Dimensional Scaling

Velocity


                       Variety
queries/sec



                           Behaviour analytics


                     Mixing with other data
                     (reviews/social+++)

               Query Complexity


                                                 Volume
               Products
                                                          7
Commoditized through OSS?

       Even Microsoft Joining?!?!??
 (MS) HPC Pack 2008 R2 Service Pack 3
“In line with our announcement in October
at the PASS conference we will focus our
effort on bringing Apache Hadoop to both
Windows Server and Windows Azure.

Hadoop has emerged as a great platform
for analyzing unstructured data or large
volumes of data at low cost…”
                                            8
Big Data – Why you may want to be interested




“By 2018, the United States alone could
face a shortage of 140,000 to 190,000
people with deep analytical skills as well
as 1.5 million managers and analysts with
the know-how to use the analysis of big
data to make effective decisions.”
“McKinsey - Big data: The next frontier for innovation, competition, and productivity “




                                                                                          9
Rakuten Japan Architecture Team


   11 Nationalities!




                                  10

More Related Content

Similar to Panel Discussion, Agile, Big Data, and Globalization

Data Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA
 
Big Data Tools: A Deep Dive into Essential Tools
Big Data Tools: A Deep Dive into Essential ToolsBig Data Tools: A Deep Dive into Essential Tools
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
 
Cubitic: Predictive Analytics
Cubitic: Predictive AnalyticsCubitic: Predictive Analytics
Cubitic: Predictive Analyticshuguk
 
Top Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practicesTop Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
 
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expo
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data ExpoDATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expo
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expowebwinkelvakdag
 
IRJET- Youtube Data Sensitivity and Analysis using Hadoop Framework
IRJET-  	  Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET-  	  Youtube Data Sensitivity and Analysis using Hadoop Framework
IRJET- Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET Journal
 
Trl jaist 20180304 v6
Trl jaist 20180304 v6Trl jaist 20180304 v6
Trl jaist 20180304 v6ISSIP
 
Top 10 renowned big data companies
Top 10 renowned big data companiesTop 10 renowned big data companies
Top 10 renowned big data companiesRobert Smith
 
The Data Platform Administration Handling the 100 PB.pdf
The Data Platform Administration Handling the 100 PB.pdfThe Data Platform Administration Handling the 100 PB.pdf
The Data Platform Administration Handling the 100 PB.pdfRakuten Group, Inc.
 
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...Antti Koskela
 
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
IRJET- Sentiment Analysis on Twitter Posts using HadoopIRJET- Sentiment Analysis on Twitter Posts using Hadoop
IRJET- Sentiment Analysis on Twitter Posts using HadoopIRJET Journal
 
Introdution to Dataops and AIOps (or MLOps)
Introdution to Dataops and AIOps (or MLOps)Introdution to Dataops and AIOps (or MLOps)
Introdution to Dataops and AIOps (or MLOps)Adrien Blind
 
Tutorial helsinki 20180313 v1
Tutorial helsinki 20180313 v1Tutorial helsinki 20180313 v1
Tutorial helsinki 20180313 v1ISSIP
 
INTERFACE, by apidays - The Evolution of Data Movement.pdf
INTERFACE, by apidays - The Evolution of Data Movement.pdfINTERFACE, by apidays - The Evolution of Data Movement.pdf
INTERFACE, by apidays - The Evolution of Data Movement.pdfapidays
 
Analytics Organizations & The New Emerging Roles
Analytics Organizations & The New Emerging RolesAnalytics Organizations & The New Emerging Roles
Analytics Organizations & The New Emerging RolesVandana Thakur
 

Similar to Panel Discussion, Agile, Big Data, and Globalization (20)

Kartikey tripathi
Kartikey tripathiKartikey tripathi
Kartikey tripathi
 
Sironta
SirontaSironta
Sironta
 
Data Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase LexsetData Con LA 2019 - Startup Showcase Lexset
Data Con LA 2019 - Startup Showcase Lexset
 
Big Data Tools: A Deep Dive into Essential Tools
Big Data Tools: A Deep Dive into Essential ToolsBig Data Tools: A Deep Dive into Essential Tools
Big Data Tools: A Deep Dive into Essential Tools
 
Cubitic: Predictive Analytics
Cubitic: Predictive AnalyticsCubitic: Predictive Analytics
Cubitic: Predictive Analytics
 
Top Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practicesTop Big data Analytics tools: Emerging trends and Best practices
Top Big data Analytics tools: Emerging trends and Best practices
 
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expo
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data ExpoDATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expo
DATAOPS: THE NEXT BIG WAVE ON YOUR DATA JOURNEY - Big Data Expo
 
IRJET- Youtube Data Sensitivity and Analysis using Hadoop Framework
IRJET-  	  Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET-  	  Youtube Data Sensitivity and Analysis using Hadoop Framework
IRJET- Youtube Data Sensitivity and Analysis using Hadoop Framework
 
Trl jaist 20180304 v6
Trl jaist 20180304 v6Trl jaist 20180304 v6
Trl jaist 20180304 v6
 
Top 10 renowned big data companies
Top 10 renowned big data companiesTop 10 renowned big data companies
Top 10 renowned big data companies
 
The Data Platform Administration Handling the 100 PB.pdf
The Data Platform Administration Handling the 100 PB.pdfThe Data Platform Administration Handling the 100 PB.pdf
The Data Platform Administration Handling the 100 PB.pdf
 
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...
Citizen Developer Tools (session at SharePoint Saturday Twin Cities 4/14/2018...
 
Our big data
Our big dataOur big data
Our big data
 
M4KNow Python Courses
M4KNow Python CoursesM4KNow Python Courses
M4KNow Python Courses
 
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
IRJET- Sentiment Analysis on Twitter Posts using HadoopIRJET- Sentiment Analysis on Twitter Posts using Hadoop
IRJET- Sentiment Analysis on Twitter Posts using Hadoop
 
Introdution to Dataops and AIOps (or MLOps)
Introdution to Dataops and AIOps (or MLOps)Introdution to Dataops and AIOps (or MLOps)
Introdution to Dataops and AIOps (or MLOps)
 
Tutorial helsinki 20180313 v1
Tutorial helsinki 20180313 v1Tutorial helsinki 20180313 v1
Tutorial helsinki 20180313 v1
 
INTERFACE, by apidays - The Evolution of Data Movement.pdf
INTERFACE, by apidays - The Evolution of Data Movement.pdfINTERFACE, by apidays - The Evolution of Data Movement.pdf
INTERFACE, by apidays - The Evolution of Data Movement.pdf
 
Big data analytics
Big data analyticsBig data analytics
Big data analytics
 
Analytics Organizations & The New Emerging Roles
Analytics Organizations & The New Emerging RolesAnalytics Organizations & The New Emerging Roles
Analytics Organizations & The New Emerging Roles
 

More from Rakuten Group, Inc.

コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話
コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話
コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話Rakuten Group, Inc.
 
楽天における安全な秘匿情報管理への道のり
楽天における安全な秘匿情報管理への道のり楽天における安全な秘匿情報管理への道のり
楽天における安全な秘匿情報管理への道のりRakuten Group, Inc.
 
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...Rakuten Group, Inc.
 
DataSkillCultureを浸透させる楽天の取り組み
DataSkillCultureを浸透させる楽天の取り組みDataSkillCultureを浸透させる楽天の取り組み
DataSkillCultureを浸透させる楽天の取り組みRakuten Group, Inc.
 
大規模なリアルタイム監視の導入と展開
大規模なリアルタイム監視の導入と展開大規模なリアルタイム監視の導入と展開
大規模なリアルタイム監視の導入と展開Rakuten Group, Inc.
 
楽天における大規模データベースの運用
楽天における大規模データベースの運用楽天における大規模データベースの運用
楽天における大規模データベースの運用Rakuten Group, Inc.
 
楽天サービスを支えるネットワークインフラストラクチャー
楽天サービスを支えるネットワークインフラストラクチャー楽天サービスを支えるネットワークインフラストラクチャー
楽天サービスを支えるネットワークインフラストラクチャーRakuten Group, Inc.
 
楽天の規模とクラウドプラットフォーム統括部の役割
楽天の規模とクラウドプラットフォーム統括部の役割楽天の規模とクラウドプラットフォーム統括部の役割
楽天の規模とクラウドプラットフォーム統括部の役割Rakuten Group, Inc.
 
Rakuten Services and Infrastructure Team.pdf
Rakuten Services and Infrastructure Team.pdfRakuten Services and Infrastructure Team.pdf
Rakuten Services and Infrastructure Team.pdfRakuten Group, Inc.
 
Supporting Internal Customers as Technical Account Managers.pdf
Supporting Internal Customers as Technical Account Managers.pdfSupporting Internal Customers as Technical Account Managers.pdf
Supporting Internal Customers as Technical Account Managers.pdfRakuten Group, Inc.
 
Making Cloud Native CI_CD Services.pdf
Making Cloud Native CI_CD Services.pdfMaking Cloud Native CI_CD Services.pdf
Making Cloud Native CI_CD Services.pdfRakuten Group, Inc.
 
How We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfHow We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfRakuten Group, Inc.
 
Travel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoTravel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoRakuten Group, Inc.
 
Travel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoTravel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoRakuten Group, Inc.
 
Introduction of GORA API Group technology
Introduction of GORA API Group technologyIntroduction of GORA API Group technology
Introduction of GORA API Group technologyRakuten Group, Inc.
 
100PBを越えるデータプラットフォームの実情
100PBを越えるデータプラットフォームの実情100PBを越えるデータプラットフォームの実情
100PBを越えるデータプラットフォームの実情Rakuten Group, Inc.
 
社内エンジニアを支えるテクニカルアカウントマネージャー
社内エンジニアを支えるテクニカルアカウントマネージャー社内エンジニアを支えるテクニカルアカウントマネージャー
社内エンジニアを支えるテクニカルアカウントマネージャーRakuten Group, Inc.
 
モニタリングプラットフォーム開発の裏側
モニタリングプラットフォーム開発の裏側モニタリングプラットフォーム開発の裏側
モニタリングプラットフォーム開発の裏側Rakuten Group, Inc.
 

More from Rakuten Group, Inc. (20)

コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話
コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話
コードレビュー改善のためにJenkinsとIntelliJ IDEAのプラグインを自作してみた話
 
楽天における安全な秘匿情報管理への道のり
楽天における安全な秘匿情報管理への道のり楽天における安全な秘匿情報管理への道のり
楽天における安全な秘匿情報管理への道のり
 
What Makes Software Green?
What Makes Software Green?What Makes Software Green?
What Makes Software Green?
 
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...
 
DataSkillCultureを浸透させる楽天の取り組み
DataSkillCultureを浸透させる楽天の取り組みDataSkillCultureを浸透させる楽天の取り組み
DataSkillCultureを浸透させる楽天の取り組み
 
大規模なリアルタイム監視の導入と展開
大規模なリアルタイム監視の導入と展開大規模なリアルタイム監視の導入と展開
大規模なリアルタイム監視の導入と展開
 
楽天における大規模データベースの運用
楽天における大規模データベースの運用楽天における大規模データベースの運用
楽天における大規模データベースの運用
 
楽天サービスを支えるネットワークインフラストラクチャー
楽天サービスを支えるネットワークインフラストラクチャー楽天サービスを支えるネットワークインフラストラクチャー
楽天サービスを支えるネットワークインフラストラクチャー
 
楽天の規模とクラウドプラットフォーム統括部の役割
楽天の規模とクラウドプラットフォーム統括部の役割楽天の規模とクラウドプラットフォーム統括部の役割
楽天の規模とクラウドプラットフォーム統括部の役割
 
Rakuten Services and Infrastructure Team.pdf
Rakuten Services and Infrastructure Team.pdfRakuten Services and Infrastructure Team.pdf
Rakuten Services and Infrastructure Team.pdf
 
Supporting Internal Customers as Technical Account Managers.pdf
Supporting Internal Customers as Technical Account Managers.pdfSupporting Internal Customers as Technical Account Managers.pdf
Supporting Internal Customers as Technical Account Managers.pdf
 
Making Cloud Native CI_CD Services.pdf
Making Cloud Native CI_CD Services.pdfMaking Cloud Native CI_CD Services.pdf
Making Cloud Native CI_CD Services.pdf
 
How We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdfHow We Defined Our Own Cloud.pdf
How We Defined Our Own Cloud.pdf
 
Travel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoTravel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech info
 
Travel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech infoTravel & Leisure Platform Department's tech info
Travel & Leisure Platform Department's tech info
 
OWASPTop10_Introduction
OWASPTop10_IntroductionOWASPTop10_Introduction
OWASPTop10_Introduction
 
Introduction of GORA API Group technology
Introduction of GORA API Group technologyIntroduction of GORA API Group technology
Introduction of GORA API Group technology
 
100PBを越えるデータプラットフォームの実情
100PBを越えるデータプラットフォームの実情100PBを越えるデータプラットフォームの実情
100PBを越えるデータプラットフォームの実情
 
社内エンジニアを支えるテクニカルアカウントマネージャー
社内エンジニアを支えるテクニカルアカウントマネージャー社内エンジニアを支えるテクニカルアカウントマネージャー
社内エンジニアを支えるテクニカルアカウントマネージャー
 
モニタリングプラットフォーム開発の裏側
モニタリングプラットフォーム開発の裏側モニタリングプラットフォーム開発の裏側
モニタリングプラットフォーム開発の裏側
 

Recently uploaded

Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxEasyPrinterHelp
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsUXDXConf
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyUXDXConf
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...FIDO Alliance
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaCzechDreamin
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...CzechDreamin
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka DoktorováCzechDreamin
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyJohn Staveley
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty SecureFemke de Vroome
 
Agentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdfAgentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdfChristopherTHyatt
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityScyllaDB
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIES VE
 
Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1DianaGray10
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101vincent683379
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKUXDXConf
 
Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastUXDXConf
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceSamy Fodil
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...FIDO Alliance
 

Recently uploaded (20)

Buy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptxBuy Epson EcoTank L3210 Colour Printer Online.pptx
Buy Epson EcoTank L3210 Colour Printer Online.pptx
 
Strategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering TeamsStrategic AI Integration in Engineering Teams
Strategic AI Integration in Engineering Teams
 
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System StrategyA Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
 
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
Choosing the Right FDO Deployment Model for Your Application _ Geoffrey at In...
 
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara LaskowskaPowerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
 
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
 
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová10 Differences between Sales Cloud and CPQ, Blanka Doktorová
10 Differences between Sales Cloud and CPQ, Blanka Doktorová
 
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John StaveleyDemystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
 
ECS 2024 Teams Premium - Pretty Secure
ECS 2024   Teams Premium - Pretty SecureECS 2024   Teams Premium - Pretty Secure
ECS 2024 Teams Premium - Pretty Secure
 
Agentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdfAgentic RAG What it is its types applications and implementation.pdf
Agentic RAG What it is its types applications and implementation.pdf
 
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through ObservabilityOptimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
 
IESVE for Early Stage Design and Planning
IESVE for Early Stage Design and PlanningIESVE for Early Stage Design and Planning
IESVE for Early Stage Design and Planning
 
Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024Enterprise Knowledge Graphs - Data Summit 2024
Enterprise Knowledge Graphs - Data Summit 2024
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1UiPath Test Automation using UiPath Test Suite series, part 1
UiPath Test Automation using UiPath Test Suite series, part 1
 
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
 
Connecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAKConnecting the Dots in Product Design at KAYAK
Connecting the Dots in Product Design at KAYAK
 
Designing for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at ComcastDesigning for Hardware Accessibility at Comcast
Designing for Hardware Accessibility at Comcast
 
WebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM PerformanceWebAssembly is Key to Better LLM Performance
WebAssembly is Key to Better LLM Performance
 
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 

Panel Discussion, Agile, Big Data, and Globalization

  • 1. Panel Discussions James Chen (Executive Officer/Vice Managing Director of Development Unit/Manager of Rakuten Ichiba Service Development & Operation Department, Rakuten, Inc.) Terje Marthinussen (Executive Officer / Vice Managing Director of Development Unit / Chief Architect of Next Generation Search, Rakuten, Inc.) Hirotaka Yoshioka (Technical Managing Officer, Rakuten Inc.) 1
  • 2. James Chen Title: Executive Officer Mission: To build the Rakuten global ecommerce platform and ecosystem – Leverage the best skills and background from around the world mitnerd – Leverage the latest technology trends (PaaS, 100% API based systems) twitter @mitnerd – Build out a globally distributed development process/teams. http://www.mitnerd .com/ Joined Rakuten since September 2009, CTO of multiple internet service related companies. Both startups and large companies Still enjoys hands-on coding!!! 2
  • 3. Terje Marthinussen Title: Executive Office/Search Architect Mission: Improve search and other large scale data processing problems. – Development, scaling, operations Joined Rakuten February 2010 (but Terje worked with Rakuten since 2003) 11 years of large scale search experience. (Fast Search & Microsoft) 19 years in large scale IT environments Worked in 4 countries on 3 continents 3
  • 4. Hiro Yoshioka Title: Technical Managing Officer Mission: Lead the technical direction of Rakuten – Improve software development productivity (introducing Agile software development). Yoshioka – Build Scalable computer – Build developers community in Rakuten and twitter @hyoshiok promote OSS strategy Joined Rakuten since August 2009, before it, he was a founding member and CTO of Miracle Linux since 2000. More than 25 years experience in the IT. OSS activist DEBUG HACKS (in Japanese), O’reilly Japan 4
  • 6. Big Data – Not a linear growth problem Gartner: 3 dimensional problem: – Volume • 13 Exabyte global data growth expected in 2010 • Annual growth 70%. – Variety • Complexity and types and sources – Velocity • Speed/frequency of data access • Cisco: Avg. annual Internet traffic +32% next 5 years 6
  • 7. Rakuten Ichiba – Multi Dimensional Scaling Velocity Variety queries/sec Behaviour analytics Mixing with other data (reviews/social+++) Query Complexity Volume Products 7
  • 8. Commoditized through OSS? Even Microsoft Joining?!?!?? (MS) HPC Pack 2008 R2 Service Pack 3 “In line with our announcement in October at the PASS conference we will focus our effort on bringing Apache Hadoop to both Windows Server and Windows Azure. Hadoop has emerged as a great platform for analyzing unstructured data or large volumes of data at low cost…” 8
  • 9. Big Data – Why you may want to be interested “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” “McKinsey - Big data: The next frontier for innovation, competition, and productivity “ 9
  • 10. Rakuten Japan Architecture Team 11 Nationalities! 10