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Data as a Service – Concepts, Design &
Implementation, and Ecosystems
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
[email protected]://dsg.tuwien.ac.at/staff/truong
1ASE Summer 2014
Advanced Services Engineering,
Summer 2014
Advanced Services Engineering,
Summer 2014
Outline
Data provisioning and data service units
Data-as-a-Service concepts
DaaS design and implementation
DaaS ecosystems
ASE Summer 2014 2
Data versus data assets
ASE Summer 20143
Data
Data Assets
Data management
and provisioning
Data concerns
Data collection,
assessment and
enrichment
Data provisioning activities and
issues
ASE Summer 2014 4
Collect
• Data sources
• Ownership
• License
• Quality assessment and enrichment
Store
• Query and backup capabilities
• Local versus cloud, distributed versus centralized storage
Access
• Interface
• Public versus private access
• Access granularity
• Pricing and licensing model
Utilize
• Alone or in combination with other data sources
• Redistribution
• Updates
Non-exhausive list! Add your own issues!
Provisioning Models
Stakeholders in data provisioning
ASE Summer 2014 5
Data
Data Provider
• People (individual/crowds/organization)
• Software, Things
Data Provider
• People (individual/crowds/organization)
• Software, Things
Service Provider
• Software and people
Service Provider
• Software and people
Data Consumer
• People, Software, Things
Data Consumer
• People, Software, Things
Data Aggregator/Integrator
• Software
• People + software
Data Aggregator/Integrator
• Software
• People + software
Data Assessment
• Software and people
Data Assessment
• Software and people
Stakeholder classes can be further divided!
Domain-specific versus domain-independent functions
Recall – Service Unit
ASE Summer 2014 6
Service model
Unit Concept
Service unit
„basic
component“/“basic
function“ modeling
and description
Consumption,
ownership,
provisioning, price, etc.
What about service units providing data?What about service units providing data?
Data service unit
ASE Summer 2014 7
Service model
Unit Concept
Data service
unit
Data
Can be used for private
or public
Can be elastic or not
What about the
granularity of
the unit?
What about the
granularity of
the unit?
Data service units in clouds/internet
Provide data capabilities rather than provide
computation or software capabilities
Providing data in clouds/internet is an increasing
trend
In both business and e-science environments
Bio data, weather data, company balance
sheets, etc., via Web services
Now often in a combination of data + analytics
atop the data
Reasons: economic benefits, performance, service
ecosystems8ASE Summer 2014
Data service unitData service unit
9
Data service units in
clouds/internet
datadata
Internet/CloudInternet/Cloud
Data service unitData service unit
People
data
Data service unitData service unit
Things
ASE Summer 2014
data data
SO DATA SERVICE UNIT IS
BIG OR SMALL? PROVIDING
REALTIME OR STATIC DATA?
Discussion time
ASE Summer 2014 10
11
NIST Cloud definitions
“This cloud model promotes availability and is
composed of five essential characteristics,
three service models, and four deployment
models.”
ASE Summer 2014
Source: NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.docSource: NIST Definition of Cloud Computing v15, http://csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc
Data as a Service -- characteristics
On-demand self-service
Capabilities to provision data at different granularities
Resource pooling
Multiple types of data, big, static or near-realtime,raw data and
high-level information
Broad network access
Can be access from anywhere
Rapid elasticity
Easy to add/remove data sources
Measured service
Measuring, monitoring and publishing data concerns and usage
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Built atop NIST‘s definition
Data-as-a-Service – service modelsData-as-a-Service – service models
Data as a Service – service models
and deployment models
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Storage-as-a-Service
(Basic storage functions)
Storage-as-a-Service
(Basic storage functions)
Database-as-a-Service
(Structured/non-structured
querying systems)
Database-as-a-Service
(Structured/non-structured
querying systems)
Data publish/subcription
middleware as a service
Data publish/subcription
middleware as a service
Sensor-as-a-ServiceSensor-as-a-Service
Private/Public/Hybrid/Community CloudsPrivate/Public/Hybrid/Community Clouds
deploy
Examples of DaaS
ASE Summer 2014 14Xively Cloud Services™
https://xively.com/
Xively Cloud Services™
https://xively.com/
WHAT ELSE DO YOU THINK
CAN BE INCLUDED INTO DAAS
MODELS?
Discussion time
ASE Summer 2014 15
DaaS design & implementation –
APIs
Read-only DaaS versus CRUD DaaS APIs
Service APIs versus Data APIs
They are not the same wrt data/service
concerns
SOAP versus REST
Streaming data API
ASE Summer 2014 16
DaaS design & implementation –
service provider vs data provider
The DaaS provider is separated from the data
provider
17
DaaS
Consumer
DaaS
Sensor
DaaS
Consumer DaaS provider Data
provider
ASE Summer 2014
Example: DaaS provider =! data
provider
18ASE Summer 2014
DaaS design & implementation –
structures
DaaS and data providers have the right to
publish the data
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DaaS
• Service APIs
• Data APIs for the whole resource
Data Resource
• Data APIs for particular resources
• Data APIs for data items
Data Items
• Data APIs for data items
Three levels
20
DaaS design & implementation –
structures (2)
Data
items
Data
items
Data
items
Data resourceData resource
Data
assets
Data resourceData resource Data resourceData resource
Data resourceData resourceData resourceData resource
Consumer
Consumer
DaaS
ASE Summer 2014
DaaS design & implementation –
patterns for „turning data to DaaS“ (1)
ASE Summer 2014 21
DaaSDaaSdatadata Build Data
Service
APIs
Deploy
Data
Service
Examples: using WSO2 data service
Storage/Database
-as-a-Service
Storage/Database
-as-a-Service
DaaS design & implementation –
patterns for „turning data to DaaS“ (2)
ASE Summer 2014 22
datadata
Examples: using
Amazon S3
DaaSDaaS
Storage/Databa
se/Middleware
Storage/Databa
se/Middleware
DaaS design & implementation –
patterns for „turning data to DaaS“ (3)
ASE Summer 2014 23
datadata
Examples:
using Crowd-
sourcing with
Pachube (the
predecessor of
Xively)
Things
One Thing 10000... Things
DaaSDaaS
Storage/Database/
Middleware
Storage/Database/
Middleware
DaaS design & implementation –
patterns for „turning data to DaaS“ (4)
ASE Summer 2014 24
datadata
Examples: using Twitter
PeopleDaaSDaaS
........
DaaS design & implementation –
not just „functional“ aspects (1)
ASE Summer 2014 25
datadata DaaSDaaS.... data assetsdata assets
Data
concerns
Quality of
dataOwnership
PriceLicense ....
EnrichmentCleansing
Profiling
Integration ...
Data Assessment
/Improvement
APIs, Querying, Data Management, etc.
DaaS design & implementation –
not just „functional“ aspects (2)
ASE Summer 2014 26
Understand the DaaS ecosystem
Specifying, Evaluating and Provisioning Data
concerns and Data Contract
In follow-up
lectures
WHAT ARE OTHER PATTERNS
IN „TURNING DATA TO
DAAS“?
Discussion time
ASE Summer 2014 27
DaaS ecosystems
ASE Summer 2014 28
Data Assessment and Enrichment
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Examples of service units in DaaS
ecosystems
ASE Summer 2014 29
Platforms/services Capabilities
Strikeiron clean, verify and validate data.
Jigsaw clean, verify and validate
business contact.
PostcodeAnywhere capture, clean, validate
and enrich business data.
Trillium Software Quality clean and standardize data
Uniserv Data Quality Solution X profile and clean data
Adeptia Integration Solution integrate data
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
DaaS ecosystem –
profiling/enriching example
ASE Summer 2014 30
http://www.strikeiron.com/
Cloud-based conceptual architecture
for data quality and enrichment
ASE Summer 2014 31
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Marco Comerio, Hong Linh Truong, Carlo Batini, Schahram Dustdar: Service-oriented data quality engineering and
data publishing in the cloud. SOCA 2010: 1-6
Data Enrichment using Web data
ASE Summer 2014 32
Source: Gomadam, K.; Yeh,
P.Z.; Verma, K.; Miller, J.A.,
"Data Enrichment Using Web
APIs," Services Economics
(SE), 2012 IEEE First
International Conference on ,
vol., no., pp.46,53, 24-29 June
2012
Source: Gomadam, K.; Yeh,
P.Z.; Verma, K.; Miller, J.A.,
"Data Enrichment Using Web
APIs," Services Economics
(SE), 2012 IEEE First
International Conference on ,
vol., no., pp.46,53, 24-29 June
2012
WHY DO YOU NEED TO STUDY
DAAS CONCEPTS, DESIGN
AND IMPLEMENTATION, AND
ECOSYSTEMS?
Discussion time
ASE Summer 2014 33
Some conceptual questions
What are the relationshipes between „data service unit“
and DaaS?
„Data service unit“ versus DaaS versus Data
Marketplace?
The unit concept supports „composability“
What does it mean „composability“ of data service
units? multiple data service units or multiple data
resources?
ASE Summer 2014 34
With the current trend on the API Management: service
providers focus on management of their API metadata
and lifecycle, is the concept of „service unit“ relevant to
API management? What are the relationships between
service units and APIs
With the current trend on the API Management: service
providers focus on management of their API metadata
and lifecycle, is the concept of „service unit“ relevant to
API management? What are the relationships between
service units and APIs
Exercises
Read mentioned papers
Check characteristics, service models and
deployment models of mentioned DaaS (and
find out more)
Identify services in the ecosystem of some DaaS
Write small programs to test public DaaS, such
as Xively, Microsoft Azure and Infochimps
Turn some data to DaaS using existing tools
ASE Summer 2014 35
36
Thanks for your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
http://dsg.tuwien.ac.at/staff/truong
ASE Summer 2014