Defining Architecture Components of
the Big Data Ecosystem

Yuri Demchenko
SNE Group, University of Amsterdam
2nd BDDAC2014 Symposium, CTS2014 Conference
19-23 May 2014, Minneapolis, USA

Outline• Big Data and Data Intensive Science as a new technology wave
– The Fourth Paradigm• Big Data definition: From 6 Vs to 5 parts• Big Data technology drivers– Where do the data come from? What are Big Data drivers?

• Big Data: Paradigm change and new challenges– From Big Data to All-Data – Moving to data centric service models• Defining Big Data Architecture Framework (BDAF)– Big Data Infrastructure (BDI) and Big Data Analytics infrastructure/tools• Summary and Discussion

BDDAC2014 @CTS2014

Big Data Architecture Framework

Slide_2

Big Data and Security Research at System andNetwork Engineering, University of Amsterdam•Long time research and development on Infrastructure services and facilities– High speed optical networking and data intensive applications– Semantic description of infrastructure and network services– Collaborative systems, Grid, Clouds and currently Big Data•

Focus on Infrastructure definition and services– Software Defined Infrastructure based on Cloud/Intercloud technologies– Dynamically provisioned security infrastructure and services• NIST Big Data Working Group– Contribution to Reference Architecture, Big Data Definition and Taxonomy, Big Data
Security• Research Data Alliance (RDA)– Interest Group on Education and Skills Development on Data Intensive Science– Big Data Analytics Interest Group• Big Data Interest Group at UvA– Non-formal but active, meets two-weekly/monthly– Provided input to NIST BD-WG and RDA activities
BDDAC2014 @CTS2014

Big Data Architecture Framework3Technology Definitions and Timeline - Overview•

Service Oriented Architecture (SOA): First proposed in 1996 and revived with
the Web Services advent in 2001-2002– Currently standard for industry, and widely used
– Provided a conceptual basis for Web Services development•

Computer Grids: Initially proposed in 1998 and finally shaped in 2003 with theOpen Grid Services Architecture (OGSA) by Open Grid Forum (OGF)– Currently remains as a collaborative environment– Migrates to cloud and inter-cloud platform•

Cloud Computing: Initially proposed in 2008 – Now in a productive phase– Defined new features, capabilities, operational/usage models and actually provided
a guidance for the new technology development– Originated from the Service Computing domain and service management focused•

Big Data and Data Intensive Science: Yet to be defined– Involves more components and processes to be included into the definition– Can be better defined as Ecosystem where data are the main driving component– Need to define the Big Data properties, expected technology capabilities and provide a
guidance/vision for future technology development

BDDAC2014 @CTS2014

Big Data Architecture Framework4Visionaries and Drivers:
Seminal works, High level reports, Activities
The Fourth Paradigm: Data-Intensive Scientific Discovery.
By Jim Gray, Microsoft, 2009. Edited by Tony Hey, et al.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/
Riding the wave: How Europe can gain fromthe rising tide of scientific data.
Final report of the High Level Expert Group on
Scientific Data. October 2010.
http://cordis.europa.eu/fp7/ict/einfrastructure/docs/hlg-sdi-report.pdf

https://www.rd-alliance.org/
NIST Big Data Working Group (NBD-WG)
https://www.rd-alliance.org/

AAA Study: Study on
AAA Platforms For
Scientificdata/information
Resources in Europe,
TERENA, UvA, LIBER,
UinvDeb.
(2011-2012)

ISO/IEC JTC1 Big Data Study Group (SGBD)
http://jtc1bigdatasg.nist.gov/home.php
BDDAC2014 @CTS2014

Big Data Architecture Framework

5The Fourth Paradigm of Scientific Research
1. Theory and logical reasoning
2. Observation or Experiment––E.g. Newton observed apples falling to design his theory ofmechanics
But Gallileo Galilei made experiments with falling objects from thePisa leaning tower

3. Simulation of theory or model–

Digital simulation can prove theory or model

4. Data-driven Scientific Discovery (aka Data Science)– More data beat hypnotized theory

BDDAC2014 @CTS2014

Big Data Architecture Framework6Gartner Technology Hypercycle (October 2013)

Big Data
Cloud Computing

Source http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
BDDAC2014 @CTS2014

Big Data Architecture Framework

7

Our/SNE Big Data Technology Research Cycle
New style of technology development
Technology consolidation

Mid-End 2013

Big Data
Cloud Computing

End 2014

2012
Mid 2014

2011

Active and productive research
Teaching on Big Data Tech/Infra
Remote BD technology following.
EU Study AAA for Research Data
Main research in Cloud/Intercloud

Component technologies mastering
Education courses development

Active research into Big Data domain definition
Building community links
Source http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
BDDAC2014 @CTS2014

Big Data Architecture Framework

8Big Data Definitions Overview•

IDC definition of Big Data (conservative and strict approach) :"A new generation of technologies and architectures designed toeconomically extract value from very large volumes of a wide variety of databy enabling high-velocity capture, discovery, and/or analysis“•

Gartner definition
Big data is high-volume, high-velocity and high-variety information assetsthat demand cost-effective, innovative forms of information processing forenhanced insight and decision making.
http://www.gartner.com/it-glossary/big-data/– Termed as 3 parts definition, not 3V definition•

Big Data: a massive volume of both structured and unstructured data that is
so large that it's difficult to process using traditional database and software
techniques.
– From “The Big Data Long Tail” blog post by Jason Bloomberg (Jan 17, 2013).
http://www.devx.com/blog/the-big-data-long-tail.html•“Data that exceeds the processing capacity of conventional databasesystems. The data is too big, moves too fast, or doesn’t fit the structures ofyour database architectures. To gain value from this data, you must choose
an alternative way to process it.”– Ed Dumbill, program chair for the O’Reilly Strata Conference

BDDAC2014 @CTS2014

Big Data Architecture Framework

9Improved: 6 (5+1) V’s of Big Data
VolumeVariety
•
•
•
•
•
•

Structured
Unstructured
Multi-factor
Probabilistic
LinkedDynamic••••

Terabytes
Records/Arch
Tables, Files
Distributed••••

Adopted in general
by NIST BD-WG
BDDAC2014 @CTS2014

Batch
Real/near-time
Processes
Streams

6 Vs ofBig Data• Changing data
• Changing model• Linkage

Variability

Velocity

•
•
•
•
•

•
•
•
•

Value

Correlations
Statistical
EventsHypothetical

Trustworthiness
Authenticity
Origin, Reputation
Availability
Accountability

Generic Big Data
Properties• Volume• Variety• Velocity
Acquired Properties(after entering system)• Value• Veracity• Variability
Commonly accepted
3V’s of Big Data

Veracity
Big Data Architecture Framework

10Big Data Definition: From 6V to 5 Parts (1)(1) Big Data Properties: 5V– Volume, Variety, Velocity, Value, Veracity– Additionally: Data Dynamicity (Variability)(2) New Data Models– Data Lifecycle and Variability– Data linking, provenance and referral integrity(3) New Analytics–

Real-time/streaming analytics, interactive and machine learning analytics(4) New Infrastructure and Tools––––

High performance Computing, Storage, Network
Heterogeneous multi-provider services integration
New Data Centric (multi-stakeholder) service models
New Data Centric security models for trusted infrastructure and data processing
and storage(5) Source and Target– High velocity/speed data capture from variety of sensors and data sources– Data delivery to different visualisation and actionable systems and consumers– Full digitised input and output, (ubiquitous) sensor networks, full digital control
BDDAC2014 @CTS2014

Big Data Architecture Framework

11

Big Data Definition: From 6V to 5 Parts (1)(1) Big Data Properties: 5V– Volume, Variety, Velocity, Value, Veracity– Additionally: Data Dynamicity (Variability)(2) New Data Models– Data linking, provenance and referral integrity– Data Lifecycle and Variability/Evolution(3) New Analytics–

Real-time/streaming analytics, interactive and machine learning analytics(4) New Infrastructure and Tools––––

High performance Computing, Storage, Network
Heterogeneous multi-provider services integration
New Data Centric (multi-stakeholder) service models
New Data Centric security models for trusted infrastructure and data processing
and storage(5) Source and Target– High velocity/speed data capture from variety of sensors and data sources– Data delivery to different visualisation and actionable systems and consumers– Full digitised input and output, (ubiquitous) sensor networks, full digital control
BDDAC2014 @CTS2014

Big Data Architecture Framework

12Big Data Definition: From 6V to 5 Parts (2)Refining Gartner definition“Big data is (1) high-volume, high-velocity and high-variety information assets thatdemand (3) cost-effective, innovative forms of information processing for (5)enhanced insight and decision making”•

Big Data (Data Intensive) Technologies are targeting to process (1) high-volume,
high-velocity, high-variety data (sets/assets) to extract intended data value andensure high-veracity of original data and obtained information that demand costeffective, innovative forms of data and information processing (analytics) for
enhanced insight, decision making, and processes control; all of those demand(should be supported by) new data models (supporting all data states and stages
during the whole data lifecycle) and new infrastructure services and tools thatallows also obtaining (and processing data) from a variety of sources (including
sensor networks) and delivering data in a variety of forms to different data andinformation consumers and devices.
(1) Big Data Properties: 5V(2) New Data Models(3) New Analytics(4) New Infrastructure and Tools(5) Source and Target

BDDAC2014 @CTS2014

Big Data Architecture Framework

13

Big Data Origin• Science• Internet, Web• Industry• Business• Living Environment,
Cities• Social media andnetworks• Healthcare• Telecom/Infrastructure

Data Transformation

Big Data Nature: Origin and Target (consumers)
Big Data Target Use• Scientific discovery• New technologies• Manufacturing,processes, transport• Personal services,
campaigns• Living environment
support• Healthcare support• Social Networking

Volume, Velocity, Variety & Value, Veracity, Variability
BDDAC2014 @CTS2014

Big Data Architecture Framework

14

Big Data technology drivers (1)• Modern e-Science in search for new knowledge– Scientific experiments and tools are becoming bigger andheavily based on data processing and mining– The long tail of science• Traditional data intensive industry– Genomic research, drugs development, Healthcare– High-tech industry, CAD/CAM, weather/climate, etc.

• Customer facing industry and companies– Advertisement, retail business, service delivery• Intelligence and security• Network/infrastructure management– Network monitoring, Intrusion detection, troubleshooting
BDDAC2014 @CTS2014

Big Data Architecture Framework

15

The Long Tail of Science (aka “Dark Data”)• Collectively “Long Tail” science is generating a lot of data
– Estimated as over 1PB per year and it is growing fast with the newtechnology proliferation• 80-20 rule: 20% users generate 80% data but not necessarily 80%
knowledge
Source: Dennis Gannon (Microsoft)
BDDAC2014 @CTS2014

Big Data Architecture Framework

NIST Big Data Workshop, 201216

Big Data technology drivers – Technology Loop

• Technology loop (known as Jevons Paradox)– Increased efficiency to process current demand will create
new uses and increase demand even more

Elastic Demand for Work:A doubling of fuelefficiency more thandoubles work demanded,
increasing the amount offuel used.
Jevons paradox occurs.

BDDAC2014 @CTS2014

Big Data Architecture Framework

17

Big Data technology drivers (2) – Managing publiccampaigns, e.g. election, public relations• The rise of public opinion stored in platforms like Twitter,
Google, Facebook, etc. provide enough intelligence toinfluence the campaign development, timing, geography andeven the colour of the campaign signs– Twitter was a major source of data aggregation for the Republican
Race in the US– Multimillion-dollar contract for data management
and collection services awarded May 1, 2013 to
Liberty Work to build advanced list of voters• Article “In Data we trust” by T.Edsall in The New York Times– Book: In Data We Trust: How Customer Data is
Revolutionising Our Economy (Aug 2012)• A strategy for tomorrow's data world

BDDAC2014 @CTS2014

Big Data Architecture Framework

18

NIST Big Data Working Group (NBD-WG) andISO/IEC JTC1 Study Group on Big Data (SGBD)• Started June 2013 - http://bigdatawg.nist.gov/home.php– Weekly calls, open participation, mailing list

• Targeted formal delivery Autumn 2014 of a set of NIST documents
http://bigdatawg.nist.gov/V1_output_docs.php
Volume 1: NIST Big Data Definitions
Volume 2: NIST Big Data Taxonomies
Volume 3: NIST Big Data Use Case & Requirements (co-chair Geoffrey Fox)
Volume 4: NIST Big Data Security and Privacy Requirements
Volume 5: NIST Big Data Architectures White Paper Survey
Volume 6: NIST Big Data Reference Architecture
Volume 7: NIST Big Data Technology Roadmap• ISO/IEC Study Group on Big Data (SGBD)
http://jtc1bigdatasg.nist.gov/home.php– Term (December 2013) – September 2014
– Extends NIST BDWG activity and scope– 2nd meeting hosted in Amsterdam 13-16 May 2014
http://jtc1bigdatasg.nist.gov/programEU.php

BDDAC2014 @CTS2014

Big Data Architecture Framework19

2nd ISO/IEC SGBD meeting 13-16 May 2014
Discussions and results• Two days workshop 13-14 May 2014
– EU and NL focus, UvA activities• Refining Big Data technology definition and Big
Data Architecture definition• New items proposed– Big Data market aspects– Data ownership (including during data lifecycle/staging
and aggregation)– Opacity (obfuscation) data linkage during processing– Data linkage

BDDAC2014 @CTS2014

Big Data Architecture Framework

20

From Big Data to All-Data – Paradigm Change• Breaking paradigm changing factor– Data storage and processing– Security– Identification and provenance• Traditional model– BIG Storage and BIG Computer with
FAT pipe– Move compute to data vs Move datato compute

Move or not
to move?

Big Data

Network?
Distributed Big Data
Storage
Data Abstraction

Data Bus• New Paradigm– Continuous data production– Continuous data processing– DataBus as a Data container andProtocol
BDDAC2014 @CTS2014

Big
Computer

VisualisationPresentationActionInfrastructure Abstraction

Distributed Computeand Analytics

Big Data Architecture Framework

DataBus:(1) Data Container(2) Metadata, State(3) Data Transfer
Protocol21

Moving to Data-Centric Models and Technologies• Current IT and communication technologies arehost based or host centric– Any communication or processing are bound to host/computer thatruns software– Especially in security: all security models are host/client based• Big Data requires new data-centric models– Data location, search, access– Data integrity and identification– Data lifecycle and variability– Data centric (declarative) programming models– Data aware infrastructure to support new data formats and data
centric programming models• Data centric security and access control
BDDAC2014 @CTS2014

Big Data Architecture Framework

22Defining Big Data Architecture Framework• Existing attempts address architecture issues in a traditional
way: ODCA, TMF, NIST
– http://bigdatawg.nist.gov/_uploadfiles/BD_Vol5_RefArchSurvey_V1Draft_Prerelease.pdf• Architecture vs Ecosystem– Big Data undergo a number of transformations during their lifecycle– Big Data fuel the whole transformation chain• Data sources and data consumers, target data usage– Multi-dimensional relations between• Data models and data driven processes• Infrastructure components and data centric services• Architecture vs Architecture Framework– Separates concerns and factors• Control and Management functions, orthogonal factors– Architecture Framework components are inter-related
BDDAC2014 @CTS2014

Big Data Architecture Framework

23

Big Data Architecture Framework (BDAF) (1)(1) Data Models, Structures, Types– Data formats, non/relational, file systems, etc.

(2) Big Data Management– Big Data Lifecycle (Management) Model• Big Data transformation/staging– Provenance, Curation, Archiving(3) Big Data Analytics and Tools– Big Data Applications• Target use, presentation, visualisation(4) Big Data Infrastructure (BDI)– Storage, Compute, (High Performance Computing,) Network– Sensor network, target/actionable devices– Big Data Operational support(5) Big Data Security– Data security in-rest, in-move, trusted processing environments
BDDAC2014 @CTS2014

Big Data Architecture Framework

24Big Data Architecture Framework (BDAF) –Aggregated – Relations between components (2)Col: Used By
Row: Requires
This

Data
Models
Structrs

Data Models& Structures

Data
Managmnt& Lifecycle

BigData
Infrastr &Operations

BigData
BigData
Analytics & Security
Applicatn

+

++

+

++

++

++

++

++

+++

Data
Managmnt &Lifecycle++

BigData
Infrastruct &Operations

+++

+++

BigData
Analytics &Applications

++

+

++

BigDataSecurity

+++

+++

+++

BDDAC2014 @CTS2014

Big Data Architecture Framework

++

+

25

Data
SourceData
Collection&
Registration

DataFilter/Enrich,Classification

Data
Analytics,
Modeling,
Prediction

Consumer

Big Data Ecosystem: General BD Infrastructure
Data Transformation, Data Management
Data
Delivery,
Visualisation

Big Data Target/Customer: Actionable/Usable Data
Target users, processes, objects, behavior, etc.
Federated
AccessandDeliveryInfrastructure(FADI)Big Data Source/Origin (sensor, experiment, logdata, behavioral data)

Big Data Analytic/Tools

Storage
GeneralPurposeComputeGeneralPurposeHigh
Performance
Computer
Clusters

Big Data Infrastructure• Heterogeneous multi-provider
inter-cloud infrastructure• Data management
infrastructure• Collaborative Environment(user/groups managements)• Advanced high performance(programmable) network• Security infrastructure

StorageSpecialised
Databases
Archives(analytics DB,
In memory,
operstional)

categories: metadata,(un)structured, (non)identifiable
Data Management Data
Data
Datacategories:
categories:metadata,
metadata,(un)structured,(un)structured,(non)identifiable(non)identifiable

Intercloud multi-provider heterogeneous Infrastructure
Security Infrastructure

BDDAC2014 @CTS2014

Network InfrastructureInternal

InfrastructureManagement/Monitoring

Big Data Architecture Framework

26

Big Data Infrastructure and Analytics Tools
Big Data Infrastructure• Heterogeneous multi-provider
inter-cloud infrastructure• Data management
infrastructure• Collaborative Environment(user/groups managements)• Advanced high performance(programmable) network• Security infrastructure

Big Data Analytics• High Performance Computer
Clusters (HPCC)• Analytics/processing: Realtime, Interactive, Batch,
Streaming• Big Data Analytics tools andapplications
BDDAC2014 @CTS2014

Big Data Architecture Framework27

Data Lifecycle/Transformation Model
Common Data Model?
•
Data Variety and Variability•
Semantic Interoperability

Data Model (1)Data Model (1)Data Model (4)Data (inter)linking?
•
PID/OID•
ORCID•
Identification•
Privacy, Opacity

Data Storage

Data
Collection&
Registration

Data
Filter/Enrich,Classification

Data
Analytics,
Modeling,
Prediction

Data
Delivery,
Visualisation

Consumer
Data Analitics
Application

Data
SourceData Model (3)Data repurposing,Analitics re-factoring,
Secondary processing••

Does Data Model changes alonglifecycle or data evolution?
Identifying and linking data

BDDAC2014 @CTS2014

–
–
–
–

Persistent identifiers
Data ownership
Traceability vs Opacity
Referral integrity

Big Data Architecture Framework28

Evolutional/Hierarchical Data Model
Actionable Data

Papers/Reports

Archival Data

Usable Data

ORCID
Processed Data (for target use)
Processed Data (for target use)
Processed Data (for target use)

PID/DOIClassified/Structured DataClassified/Structured DataClassified/Structured Data

Raw Data
Topics for discussion, research andstandardisation

BDDAC2014 @CTS2014

•
•
•
•

Common Data Model?
Data interlinking?
Fits to Graph data type?
Metadata
Big Data Architecture Framework

•
•
•
•

Referrals
Control information
PolicyData patterns
29Summary and topics for discussion• Researching, learning mastering Big Data domain is a Big
Data problem itself• Cloud Computing as a natural platform for Big Data
– Acceptance of clouds will grow, so demand for specialists• New generically data centric models are required• New distributed data processing and analytics computing
models to be developed/re-factored• Data Scientist is a new focus for talents search bycompanies and task for universities to develop a new
curriculum

BDDAC2014 @CTS2014

Big Data Architecture Framework30Additional topics• Data Scientist: New profession and need for
Education&Training

BDDAC2014 @CTS2014

Big Data Architecture Framework31Data Scientist: New Profession and Opportunities
McKinsey Institute on Big Data Jobs (2011)
http://www.mckinsey.com/mgi/publications/big_data/index.asp•

There will be a shortage of talent necessary for organizations to take advantage
of Big Data.
––

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 makeeffective decisions

SOURCE:US Bureau of Labor Statistics; US Census; Dun & Bradstreet; company interviews; McKinsey analysis
Wsh SGBD, 13-16 May 2014

Big Data and Education

32

Strata Survey Skills and Data Scientist Self-ID

Analysing the Analysers. O’Reilly Strata
Survey – Harris, Murphy & Vaisman, 2013•
Based on how data scientists think
about themselves and their work•
Identified four Data Scientist clusters

Wsh SGBD, 13-16 May 2014

Big Data and Education

33

Skills and Self-ID Top Factors
Business

ML/BigData
Math/OR
Programming
Statistics

ML – Machine Learning
OR – Operations Research
Wsh SGBD, 13-16 May 2014

Big Data and Education

34

Slide from the presentation
Demystifying Data Science(by Natasha Balac, SDSC)

Wsh SGBD, 13-16 May 2014

Big Data and Education

35

Key to a Great Data Scientist
Technical skills (Coding, Statistics, Math)+ Commitment +Creativity+ Intuition+ Presentation Skills+ Business Savvy= Great Data Scientist!
• How Long Does It Take For a Beginner to Become a Good
Data Scientist?
– 3-5 years according to KDnuggets survey [278 votes total]

Wsh SGBD, 13-16 May 2014

Big Data and Education

36