SlideShare a Scribd company logo
1 of 21
Download to read offline
April, 2014
From Business Idea to Successful Delivery
▪ Leading global Product and
Application Development
partner founded in 1993
▪ 3,200 employees across North
America, Ukraine, Russia and
Western Europe
▪ Thousands of successful
outsourcing projects!
Clients include:
SaaS/Cloud Solutions . Mobility Solutions . UX/UI
BI/Analytics/Big Data . Software Architecture . Security
Agenda
Product
Development
Lifecycle
Big Data
Reference
Architectures
Case Studies
Tips for Successful
Delivery
Big Data
Successful Ideas
Top 10 startups with
most funding
Big Data will drive $232 billion in IT spending
through 2016
(Gartner)
BIG DATA
INVESTMENTS
2008-2012
$ 4.9B
2013
Jan-Oct
$ 3.6B
Source: www.bigdata-startups.com
Big Data Use Cases
Spotify – Changing
the music industry
Heineken –
Shopperception analyses
From In-house To Open
Innovation
Product development
lifecycle
1. Envisioning & birth of idea
2. Probing user needs
3. Ideation & design
4. Conceptualization
5. Feasibility study
6. Strategizing
7. Business analysis
8. PoC, Prototyping
9. Final design
10. Technical implementation
11. Pilot production
12. Commercialization
13. Pricing
14. Maintenance & support
15. Extension thru innovation
16. Next version, goto 1
17. R.I.P.
time span
GENERICGENERICSPECIFIC
process agility
1. Envisioning & birth of idea
2. Probing user needs
3. Ideation & design
4. Conceptualization
5. Feasibility study
6. Strategizing
7. Business analysis
8. PoC, Prototyping
9. Final design
10. Technical implementation
11. Pilot production
12. Commercialization
13. Pricing
14. Maintenance & support
15. Extension thru innovation
16. Next version, goto 1
17. R.I.P.
From In-house To Open
Innovation
Product development
lifecycle
time span
GENERICGENERICSPECIFIC
process agility
Reference Architectures:
▪ Extended Relational
▪ Non-Relational
▪ Hybrid
Big Data Analytics
Reference Architectures
Architecture Drivers:
▪ Volume
▪ Sources
▪ Throughput
▪ Latency
▪ Extensibility
▪ Data Quality
▪ Reliability
▪ Security
▪ Self-Service
▪ Cost
Relational Reference
Architecture
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Advanced
Analytics
OLAP Cubes
Query &
Reporting
Staging
Areas
Operational
Data Stores
Data Marts
Data
Warehouses
Replication
API/ODBC
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured
Extended Relational
Reference Architecture
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Advanced
Analytics
OLAP Cubes
Query &
Reporting
Staging
Areas
Operational
Data Stores
Data Marts
Data
Warehouses
Replication
API/ODBC
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured
Non-Relational
Reference Architecture
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Advanced
Analytics
Map Reduce
Query &
Reporting
Search Engines
Distributed File
Systems
NoSQL
Databases
API
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured
Hybrid Reference
Architecture
Data Refinery Lambda Architecture
Source:Source:
Relational vs. Non-
Relational Architecture
Requirements Relational
Non-
Relational
Comments
Big Data Scalability Using the MPP techniques, relational data warehouses can run terabytes and
even petabyte+ data. NoSQL solutions can scale further keeping dozens of
petabytes.
Ad-Hoc Reporting There is a variety of mature SQL BI tools in the market. At the same time, BI tools
and connectors for NoSQL databases continue evolving.
Near-Real Time Data Latency Although near-real time is achievable in both relational data warehouse and
NoSQL solutions, it requires extra efforts optimizing data update and processing.
Processing Raw Unstructured Data The benefit of NoSQL solutions (including the Hadoop ecosystem) is easy storing
and processing of unstructured data.
High Data Model Extensibility NoSQL schema-less nature allows easy extending of the data model with the
new data attributes on the fly. Extending relational storages is still possible with
design patterns, but it is quite limited and difficult if compared to NoSQL.
Reliability and Fault-Tolerance Most of the relational and NoSQL technologies offer reliable solutions replicating
data between redundant instances and supporting failover.
High Data Quality and Consistency The RDBMS solutions are about the ACID concept, while NoSQL are about the
BASE concept.
This way, most of NoSQL solutions sacrifice consistency over availability, limiting
their usage in quality critical applications.
High Security and Regulatory
Compliance
Relational storages are traditionally strong in security as they offer role-based
access, row-level security and encryption of data in transit and data in rest. Most
of today’s NoSQL solutions have significant deficit of such features and rely on a
perimeter security model.
Low Cost There are at least two factors that make NoSQL solutions less costly than
relational ones:
1) $0 or considerably low license fee due to open source
2) NoSQL databases typically use clusters of cheap commodity servers
Skills Availability There is still an experience gap with NoSQL technologies on the IT labor market
and the correspondent lack of in-house skills within organizations.
Relational vs. Non-
Relational Architecture
Relational Non-Relational
• Rational
• Predictable
• Traditional
• Flexible
• Agile
• Modern
Business Goals:
Provide visual environment for building
custom mobile application
 Charge customers based on the platform
they are using, number of consumers’
applications etc.
Business Area:
Cloud based platform for building, deploying,
hosting and managing of mobile applications
Case Study #1: Usage & Billing Analysis
1. Envisioning & birth of idea
2. Probing user needs
3. Ideation & design
4. Conceptualization
5. Feasibility study
6. Strategizing
7. Business analysis
8. PoC, Prototyping
9. Final design
10. Technical implementation
11. Pilot production
12. Commercialization
13. Pricing
14. Maintenance & support
15. Extension thru innovation
16. Next version, goto 1
17. R.I.P.
SoftServe Involvement Product development
lifecycle
From In-house
To Open Innovation
with SoftServe
Technologies:
 Python
 Amazon Redshift
 Amazon SQS
 Amazon S3
 Elastic Beanstalk
 Jaspersoft BI Professional
 Aria Subscription Billing
Platform
▪ Volume (> 10 TB)
▪ Sources (JSON)
▪ Throughput (> 10K/sec)
▪ Latency (2 min)
▪ Extensibility (Custom metrics)
▪ Data Quality (Consistency)
▪ Reliability (24/7)
▪ Security (Multitenancy)
▪ Self-Service (Ad-Hoc reports)
▪ Cost (The less the better )
▪ Constraints (AWS)
Architecture Drivers:
Business Goals:
 In addition to main service to build in-house Analytics
Platform for ROI measurement and performance analysis of
every product and feature delivered by the platform;
 Platform should provide the ability to understand how
end-users are interacting with service content, products, and
features on sites;
 Take ownership of analytics event stream;
 Perform A/B Testing
Business Area:
Retail. A platform for e-commerce and
collecting feedbacks from customers
Case Study #2: Clickstream for retail website
1. Envisioning & birth of idea
2. Probing user needs
3. Ideation & design
4. Conceptualization
5. Feasibility study
6. Strategizing
7. Business analysis
8. PoC, Prototyping
9. Final design
10. Technical implementation
11. Pilot production
12. Commercialization
13. Pricing
14. Maintenance & support
15. Extension thru innovation
16. Next version, goto 1
17. R.I.P.
SoftServe Involvement Product development
lifecycle
From In-house
To Open Innovation
with SoftServe
Technologies:
• Amazon Elastic Load
Balancer, S3
• Tornado, Flume
• Hadoop/HDFS, HBase,
MapReduce, Oozie
(Cloudera distribution)
• Backbone
▪ Volume (45 TB)
▪ Sources (JSON)
▪ Throughput (> 10K/sec)
▪ Latency (1 hour)
▪ Extensibility (Custom tags)
▪ Data Quality (Not critical)
▪ Reliability (24/7)
▪ Security (Multitenancy)
▪ Self-Service (Canned reports)
▪ Cost (The less the better )
▪ Constraints (AWS)
Architecture Drivers:
 Understand in-house Big Data capabilities
 Determine what steps from Product development lifecycle
can be done with partners
 Establish collaboration approach
 Do feasibility study
 Create architecture and select technology stack
 Do prototyping, re-evaluate architecture
 Estimate implementation efforts
 Align project milestones and success criteria
 Align team structure and processes
 Set up transparent knowledge management environment
 Set up DevOps practices from the very beginning
 Advance in Product development through “Small Wins”
Checklist for Successful
Delivery
SoftServe UK Office
Regent's Place,
338 Euston Road,
London, NW1 3BT
Tel: +44 203 519 1216
Contacts
Serhiy Haziyev: shaziyev@softserveinc.com
Olha Hrytsay: ohrytsay@softserveinc.com
Glen Wilson: gwil@softserveinc.com
Thank You!

More Related Content

What's hot

Fintech workshop Part I - Law Society of Hong Kong - Xccelerate
Fintech workshop Part I - Law Society of Hong Kong - XccelerateFintech workshop Part I - Law Society of Hong Kong - Xccelerate
Fintech workshop Part I - Law Society of Hong Kong - XccelerateHenrique Centieiro
 
Big Data: Real-life Examples of Business Value Generation
Big Data: Real-life Examples of Business Value GenerationBig Data: Real-life Examples of Business Value Generation
Big Data: Real-life Examples of Business Value GenerationCapgemini
 
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Molly Alexander
 
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014Findwise
 
Artificial Intelligence and Digital Banking - What about fraud prevention ?
Artificial Intelligence and Digital Banking - What about fraud prevention ?Artificial Intelligence and Digital Banking - What about fraud prevention ?
Artificial Intelligence and Digital Banking - What about fraud prevention ?Jérôme Kehrli
 
Building new business models through big data dec 06 2012
Building new business models through big data   dec 06 2012Building new business models through big data   dec 06 2012
Building new business models through big data dec 06 2012Aki Balogh
 
Moving From Insight to Action
Moving From Insight to ActionMoving From Insight to Action
Moving From Insight to ActionInvoca
 
Big Data Startups - Top Visualization and Data Analytics Startups
Big Data Startups - Top Visualization and Data Analytics StartupsBig Data Startups - Top Visualization and Data Analytics Startups
Big Data Startups - Top Visualization and Data Analytics Startupswallesplace
 
Creating $100 million from Big Data Analytics in Banking
Creating $100 million from Big Data Analytics in BankingCreating $100 million from Big Data Analytics in Banking
Creating $100 million from Big Data Analytics in BankingGuy Pearce
 
Big Data Industry Insights 2015
Big Data Industry Insights 2015 Big Data Industry Insights 2015
Big Data Industry Insights 2015 Den Reymer
 
Adopting Analytics in Telecom
Adopting Analytics in TelecomAdopting Analytics in Telecom
Adopting Analytics in TelecomBitanshu Das
 
The Journey to Big Data Analytics
The Journey to Big Data AnalyticsThe Journey to Big Data Analytics
The Journey to Big Data AnalyticsDr.Stefan Radtke
 
7 Steps Big Data Journey for Enterprises
7 Steps Big Data Journey for Enterprises7 Steps Big Data Journey for Enterprises
7 Steps Big Data Journey for EnterprisesRaju Shreewastava
 
Big Risks Requires Big Data Thinking
Big Risks Requires Big Data ThinkingBig Risks Requires Big Data Thinking
Big Risks Requires Big Data ThinkingTableau Software
 
Fueling the future of fintech with data science and ai
Fueling the future of fintech with data science and aiFueling the future of fintech with data science and ai
Fueling the future of fintech with data science and aiIndusNetMarketing
 
IBM Banking videocast - 3/20/2013
IBM Banking videocast - 3/20/2013 IBM Banking videocast - 3/20/2013
IBM Banking videocast - 3/20/2013 Casey Lucas
 
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014MassTLC
 
Data Monetization: Leveraging Subscriber Data to Create New Opportunities
Data Monetization: Leveraging Subscriber Data to Create New OpportunitiesData Monetization: Leveraging Subscriber Data to Create New Opportunities
Data Monetization: Leveraging Subscriber Data to Create New OpportunitiesCartesian (formerly CSMG)
 
Pi cube banking on predictive analytics151
Pi cube   banking on predictive analytics151Pi cube   banking on predictive analytics151
Pi cube banking on predictive analytics151Cole Capital
 

What's hot (20)

Fintech workshop Part I - Law Society of Hong Kong - Xccelerate
Fintech workshop Part I - Law Society of Hong Kong - XccelerateFintech workshop Part I - Law Society of Hong Kong - Xccelerate
Fintech workshop Part I - Law Society of Hong Kong - Xccelerate
 
Big Data: Real-life Examples of Business Value Generation
Big Data: Real-life Examples of Business Value GenerationBig Data: Real-life Examples of Business Value Generation
Big Data: Real-life Examples of Business Value Generation
 
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
Towards the Next Generation Financial Crimes Platform - How Data, Analytics, ...
 
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
IBM Big Data Analytics - Cognitive Computing and Watson - Findability Day 2014
 
Artificial Intelligence and Digital Banking - What about fraud prevention ?
Artificial Intelligence and Digital Banking - What about fraud prevention ?Artificial Intelligence and Digital Banking - What about fraud prevention ?
Artificial Intelligence and Digital Banking - What about fraud prevention ?
 
Building new business models through big data dec 06 2012
Building new business models through big data   dec 06 2012Building new business models through big data   dec 06 2012
Building new business models through big data dec 06 2012
 
Moving From Insight to Action
Moving From Insight to ActionMoving From Insight to Action
Moving From Insight to Action
 
Big Data Startups - Top Visualization and Data Analytics Startups
Big Data Startups - Top Visualization and Data Analytics StartupsBig Data Startups - Top Visualization and Data Analytics Startups
Big Data Startups - Top Visualization and Data Analytics Startups
 
Monetize Big Data
Monetize Big DataMonetize Big Data
Monetize Big Data
 
Creating $100 million from Big Data Analytics in Banking
Creating $100 million from Big Data Analytics in BankingCreating $100 million from Big Data Analytics in Banking
Creating $100 million from Big Data Analytics in Banking
 
Big Data Industry Insights 2015
Big Data Industry Insights 2015 Big Data Industry Insights 2015
Big Data Industry Insights 2015
 
Adopting Analytics in Telecom
Adopting Analytics in TelecomAdopting Analytics in Telecom
Adopting Analytics in Telecom
 
The Journey to Big Data Analytics
The Journey to Big Data AnalyticsThe Journey to Big Data Analytics
The Journey to Big Data Analytics
 
7 Steps Big Data Journey for Enterprises
7 Steps Big Data Journey for Enterprises7 Steps Big Data Journey for Enterprises
7 Steps Big Data Journey for Enterprises
 
Big Risks Requires Big Data Thinking
Big Risks Requires Big Data ThinkingBig Risks Requires Big Data Thinking
Big Risks Requires Big Data Thinking
 
Fueling the future of fintech with data science and ai
Fueling the future of fintech with data science and aiFueling the future of fintech with data science and ai
Fueling the future of fintech with data science and ai
 
IBM Banking videocast - 3/20/2013
IBM Banking videocast - 3/20/2013 IBM Banking videocast - 3/20/2013
IBM Banking videocast - 3/20/2013
 
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
McKinsey MassTLC Big Data Seminar Keynote - February 28, 2014
 
Data Monetization: Leveraging Subscriber Data to Create New Opportunities
Data Monetization: Leveraging Subscriber Data to Create New OpportunitiesData Monetization: Leveraging Subscriber Data to Create New Opportunities
Data Monetization: Leveraging Subscriber Data to Create New Opportunities
 
Pi cube banking on predictive analytics151
Pi cube   banking on predictive analytics151Pi cube   banking on predictive analytics151
Pi cube banking on predictive analytics151
 

Similar to From Business Idea to Successful Delivery by Serhiy Haziyev & Olha Hrytsay, SoftServe

Achieve New Heights with Modern Analytics
Achieve New Heights with Modern AnalyticsAchieve New Heights with Modern Analytics
Achieve New Heights with Modern AnalyticsSense Corp
 
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...Driving Network and Marketing Investments at O2 by Focusing on Improving the ...
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...DataWorks Summit
 
Digital Reinvention by NRB
Digital Reinvention by NRBDigital Reinvention by NRB
Digital Reinvention by NRBWilliam Poos
 
It Consulting & Services - Black Basil Technologies
It Consulting & Services  - Black Basil TechnologiesIt Consulting & Services  - Black Basil Technologies
It Consulting & Services - Black Basil TechnologiesBlack Basil Technologies
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningProvectus
 
Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
Overcoming Data Gravity in Multi-Cloud Enterprise ArchitecturesOvercoming Data Gravity in Multi-Cloud Enterprise Architectures
Overcoming Data Gravity in Multi-Cloud Enterprise ArchitecturesVMware Tanzu
 
Accelerate Business Agility with PaaS
Accelerate Business Agility with PaaS Accelerate Business Agility with PaaS
Accelerate Business Agility with PaaS WSO2
 
flexpod_hadoop_cloudera
flexpod_hadoop_clouderaflexpod_hadoop_cloudera
flexpod_hadoop_clouderaPrem Jain
 
Connecta Event: Big Query och dataanalys med Google Cloud Platform
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnecta Event: Big Query och dataanalys med Google Cloud Platform
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
 
Microsoft cloud big data strategy
Microsoft cloud big data strategyMicrosoft cloud big data strategy
Microsoft cloud big data strategyJames Serra
 
Schnellere Digitalisierung mit einer cloudbasierten Datenstrategie
Schnellere Digitalisierung mit einer cloudbasierten DatenstrategieSchnellere Digitalisierung mit einer cloudbasierten Datenstrategie
Schnellere Digitalisierung mit einer cloudbasierten DatenstrategieMongoDB
 
Transforming Business in a Digital Era with Big Data and Microsoft
Transforming Business in a Digital Era with Big Data and MicrosoftTransforming Business in a Digital Era with Big Data and Microsoft
Transforming Business in a Digital Era with Big Data and MicrosoftPerficient, Inc.
 
Data Treatment MongoDB
Data Treatment MongoDBData Treatment MongoDB
Data Treatment MongoDBNorberto Leite
 
The Essentials Of Project Management
The Essentials Of Project ManagementThe Essentials Of Project Management
The Essentials Of Project ManagementLaura Arrigo
 
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...Hortonworks
 
Extending open source and hybrid cloud to drive OT transformation - Future Oi...
Extending open source and hybrid cloud to drive OT transformation - Future Oi...Extending open source and hybrid cloud to drive OT transformation - Future Oi...
Extending open source and hybrid cloud to drive OT transformation - Future Oi...John Archer
 

Similar to From Business Idea to Successful Delivery by Serhiy Haziyev & Olha Hrytsay, SoftServe (20)

Achieve New Heights with Modern Analytics
Achieve New Heights with Modern AnalyticsAchieve New Heights with Modern Analytics
Achieve New Heights with Modern Analytics
 
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...Driving Network and Marketing Investments at O2 by Focusing on Improving the ...
Driving Network and Marketing Investments at O2 by Focusing on Improving the ...
 
Digital Reinvention by NRB
Digital Reinvention by NRBDigital Reinvention by NRB
Digital Reinvention by NRB
 
It Consulting & Services - Black Basil Technologies
It Consulting & Services  - Black Basil TechnologiesIt Consulting & Services  - Black Basil Technologies
It Consulting & Services - Black Basil Technologies
 
KEDAR_TERDALKAR
KEDAR_TERDALKARKEDAR_TERDALKAR
KEDAR_TERDALKAR
 
Feature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine LearningFeature Store as a Data Foundation for Machine Learning
Feature Store as a Data Foundation for Machine Learning
 
Rushabh_Doshi_1_
Rushabh_Doshi_1_Rushabh_Doshi_1_
Rushabh_Doshi_1_
 
Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
Overcoming Data Gravity in Multi-Cloud Enterprise ArchitecturesOvercoming Data Gravity in Multi-Cloud Enterprise Architectures
Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
 
Accelerate Business Agility with PaaS
Accelerate Business Agility with PaaS Accelerate Business Agility with PaaS
Accelerate Business Agility with PaaS
 
flexpod_hadoop_cloudera
flexpod_hadoop_clouderaflexpod_hadoop_cloudera
flexpod_hadoop_cloudera
 
Connecta Event: Big Query och dataanalys med Google Cloud Platform
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnecta Event: Big Query och dataanalys med Google Cloud Platform
Connecta Event: Big Query och dataanalys med Google Cloud Platform
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data Architecture
 
Microsoft cloud big data strategy
Microsoft cloud big data strategyMicrosoft cloud big data strategy
Microsoft cloud big data strategy
 
Schnellere Digitalisierung mit einer cloudbasierten Datenstrategie
Schnellere Digitalisierung mit einer cloudbasierten DatenstrategieSchnellere Digitalisierung mit einer cloudbasierten Datenstrategie
Schnellere Digitalisierung mit einer cloudbasierten Datenstrategie
 
Transforming Business in a Digital Era with Big Data and Microsoft
Transforming Business in a Digital Era with Big Data and MicrosoftTransforming Business in a Digital Era with Big Data and Microsoft
Transforming Business in a Digital Era with Big Data and Microsoft
 
Data Treatment MongoDB
Data Treatment MongoDBData Treatment MongoDB
Data Treatment MongoDB
 
The Essentials Of Project Management
The Essentials Of Project ManagementThe Essentials Of Project Management
The Essentials Of Project Management
 
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...
C-BAG Big Data Meetup Chennai Oct.29-2014 Hortonworks and Concurrent on Casca...
 
Resume
ResumeResume
Resume
 
Extending open source and hybrid cloud to drive OT transformation - Future Oi...
Extending open source and hybrid cloud to drive OT transformation - Future Oi...Extending open source and hybrid cloud to drive OT transformation - Future Oi...
Extending open source and hybrid cloud to drive OT transformation - Future Oi...
 

More from SoftServe

Approaching Quality in Digital Era
Approaching Quality in Digital EraApproaching Quality in Digital Era
Approaching Quality in Digital EraSoftServe
 
Digital Product Security
Digital Product SecurityDigital Product Security
Digital Product SecuritySoftServe
 
Testing Tools and Tips
Testing Tools and TipsTesting Tools and Tips
Testing Tools and TipsSoftServe
 
Android Mobile Application Testing: Human Interface Guideline, Tools
Android Mobile Application Testing: Human Interface Guideline, ToolsAndroid Mobile Application Testing: Human Interface Guideline, Tools
Android Mobile Application Testing: Human Interface Guideline, ToolsSoftServe
 
Android Mobile Application Testing: Specific Functional, Performance, Device ...
Android Mobile Application Testing: Specific Functional, Performance, Device ...Android Mobile Application Testing: Specific Functional, Performance, Device ...
Android Mobile Application Testing: Specific Functional, Performance, Device ...SoftServe
 
How to Reduce Time to Market Using Microsoft DevOps Solutions
How to Reduce Time to Market Using Microsoft DevOps SolutionsHow to Reduce Time to Market Using Microsoft DevOps Solutions
How to Reduce Time to Market Using Microsoft DevOps SolutionsSoftServe
 
Containerization: The DevOps Revolution
Containerization: The DevOps Revolution Containerization: The DevOps Revolution
Containerization: The DevOps Revolution SoftServe
 
Essential Data Engineering for Data Scientist
Essential Data Engineering for Data Scientist Essential Data Engineering for Data Scientist
Essential Data Engineering for Data Scientist SoftServe
 
Rapid Prototyping for Big Data with AWS
Rapid Prototyping for Big Data with AWS Rapid Prototyping for Big Data with AWS
Rapid Prototyping for Big Data with AWS SoftServe
 
Implementing Test Automation: What a Manager Should Know
Implementing Test Automation: What a Manager Should KnowImplementing Test Automation: What a Manager Should Know
Implementing Test Automation: What a Manager Should KnowSoftServe
 
Using AWS Lambda for Infrastructure Automation and Beyond
Using AWS Lambda for Infrastructure Automation and BeyondUsing AWS Lambda for Infrastructure Automation and Beyond
Using AWS Lambda for Infrastructure Automation and BeyondSoftServe
 
Advanced Analytics and Data Science Expertise
Advanced Analytics and Data Science ExpertiseAdvanced Analytics and Data Science Expertise
Advanced Analytics and Data Science ExpertiseSoftServe
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachSoftServe
 
Big Data as a Service: A Neo-Metropolis Model Approach for Innovation
Big Data as a Service: A Neo-Metropolis Model Approach for InnovationBig Data as a Service: A Neo-Metropolis Model Approach for Innovation
Big Data as a Service: A Neo-Metropolis Model Approach for InnovationSoftServe
 
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...SoftServe
 
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...SoftServe
 
Managing Requirements with Word and TFS by Max Markov
Managing Requirements with Word and TFS by Max MarkovManaging Requirements with Word and TFS by Max Markov
Managing Requirements with Word and TFS by Max MarkovSoftServe
 
How to Implement Hybrid Cloud Solutions Successfully
How to Implement Hybrid Cloud Solutions SuccessfullyHow to Implement Hybrid Cloud Solutions Successfully
How to Implement Hybrid Cloud Solutions SuccessfullySoftServe
 
Designing Big Data Systems Like a Pro
Designing Big Data Systems Like a ProDesigning Big Data Systems Like a Pro
Designing Big Data Systems Like a ProSoftServe
 
Product Management in Outsourcing by Roman Kolodchak and Roman Pavlyuk
Product Management in Outsourcing by Roman Kolodchak and Roman PavlyukProduct Management in Outsourcing by Roman Kolodchak and Roman Pavlyuk
Product Management in Outsourcing by Roman Kolodchak and Roman PavlyukSoftServe
 

More from SoftServe (20)

Approaching Quality in Digital Era
Approaching Quality in Digital EraApproaching Quality in Digital Era
Approaching Quality in Digital Era
 
Digital Product Security
Digital Product SecurityDigital Product Security
Digital Product Security
 
Testing Tools and Tips
Testing Tools and TipsTesting Tools and Tips
Testing Tools and Tips
 
Android Mobile Application Testing: Human Interface Guideline, Tools
Android Mobile Application Testing: Human Interface Guideline, ToolsAndroid Mobile Application Testing: Human Interface Guideline, Tools
Android Mobile Application Testing: Human Interface Guideline, Tools
 
Android Mobile Application Testing: Specific Functional, Performance, Device ...
Android Mobile Application Testing: Specific Functional, Performance, Device ...Android Mobile Application Testing: Specific Functional, Performance, Device ...
Android Mobile Application Testing: Specific Functional, Performance, Device ...
 
How to Reduce Time to Market Using Microsoft DevOps Solutions
How to Reduce Time to Market Using Microsoft DevOps SolutionsHow to Reduce Time to Market Using Microsoft DevOps Solutions
How to Reduce Time to Market Using Microsoft DevOps Solutions
 
Containerization: The DevOps Revolution
Containerization: The DevOps Revolution Containerization: The DevOps Revolution
Containerization: The DevOps Revolution
 
Essential Data Engineering for Data Scientist
Essential Data Engineering for Data Scientist Essential Data Engineering for Data Scientist
Essential Data Engineering for Data Scientist
 
Rapid Prototyping for Big Data with AWS
Rapid Prototyping for Big Data with AWS Rapid Prototyping for Big Data with AWS
Rapid Prototyping for Big Data with AWS
 
Implementing Test Automation: What a Manager Should Know
Implementing Test Automation: What a Manager Should KnowImplementing Test Automation: What a Manager Should Know
Implementing Test Automation: What a Manager Should Know
 
Using AWS Lambda for Infrastructure Automation and Beyond
Using AWS Lambda for Infrastructure Automation and BeyondUsing AWS Lambda for Infrastructure Automation and Beyond
Using AWS Lambda for Infrastructure Automation and Beyond
 
Advanced Analytics and Data Science Expertise
Advanced Analytics and Data Science ExpertiseAdvanced Analytics and Data Science Expertise
Advanced Analytics and Data Science Expertise
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
 
Big Data as a Service: A Neo-Metropolis Model Approach for Innovation
Big Data as a Service: A Neo-Metropolis Model Approach for InnovationBig Data as a Service: A Neo-Metropolis Model Approach for Innovation
Big Data as a Service: A Neo-Metropolis Model Approach for Innovation
 
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...
Personalized Medicine in a Contemporary World by Eugene Borukhovich, SVP Heal...
 
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...
Health 2.0 WinterTech: Will Artificial Intelligence change healthcare? by Eug...
 
Managing Requirements with Word and TFS by Max Markov
Managing Requirements with Word and TFS by Max MarkovManaging Requirements with Word and TFS by Max Markov
Managing Requirements with Word and TFS by Max Markov
 
How to Implement Hybrid Cloud Solutions Successfully
How to Implement Hybrid Cloud Solutions SuccessfullyHow to Implement Hybrid Cloud Solutions Successfully
How to Implement Hybrid Cloud Solutions Successfully
 
Designing Big Data Systems Like a Pro
Designing Big Data Systems Like a ProDesigning Big Data Systems Like a Pro
Designing Big Data Systems Like a Pro
 
Product Management in Outsourcing by Roman Kolodchak and Roman Pavlyuk
Product Management in Outsourcing by Roman Kolodchak and Roman PavlyukProduct Management in Outsourcing by Roman Kolodchak and Roman Pavlyuk
Product Management in Outsourcing by Roman Kolodchak and Roman Pavlyuk
 

Recently uploaded

eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionsNirav Modi
 
Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Incrobinwilliams8624
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxJoão Esperancinha
 
Streamlining Your Application Builds with Cloud Native Buildpacks
Streamlining Your Application Builds  with Cloud Native BuildpacksStreamlining Your Application Builds  with Cloud Native Buildpacks
Streamlining Your Application Builds with Cloud Native BuildpacksVish Abrams
 
How Does the Epitome of Spyware Differ from Other Malicious Software?
How Does the Epitome of Spyware Differ from Other Malicious Software?How Does the Epitome of Spyware Differ from Other Malicious Software?
How Does the Epitome of Spyware Differ from Other Malicious Software?AmeliaSmith90
 
JS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIJS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIIvo Andreev
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Jaydeep Chhasatia
 
Watermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesWatermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesShyamsundar Das
 
ERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxAutus Cyber Tech
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsJaydeep Chhasatia
 
Why Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfWhy Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfBrain Inventory
 
Kawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfTobias Schneck
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadIvo Andreev
 
Generative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilGenerative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilVICTOR MAESTRE RAMIREZ
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampVICTOR MAESTRE RAMIREZ
 
Growing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesGrowing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesSoftwareMill
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdfMeon Technology
 
Top Software Development Trends in 2024
Top Software Development Trends in  2024Top Software Development Trends in  2024
Top Software Development Trends in 2024Mind IT Systems
 
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine HarmonyLeveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmonyelliciumsolutionspun
 

Recently uploaded (20)

eAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspectionseAuditor Audits & Inspections - conduct field inspections
eAuditor Audits & Inspections - conduct field inspections
 
Enterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze IncEnterprise Document Management System - Qualityze Inc
Enterprise Document Management System - Qualityze Inc
 
Fields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptxFields in Java and Kotlin and what to expect.pptx
Fields in Java and Kotlin and what to expect.pptx
 
Streamlining Your Application Builds with Cloud Native Buildpacks
Streamlining Your Application Builds  with Cloud Native BuildpacksStreamlining Your Application Builds  with Cloud Native Buildpacks
Streamlining Your Application Builds with Cloud Native Buildpacks
 
How Does the Epitome of Spyware Differ from Other Malicious Software?
How Does the Epitome of Spyware Differ from Other Malicious Software?How Does the Epitome of Spyware Differ from Other Malicious Software?
How Does the Epitome of Spyware Differ from Other Malicious Software?
 
JS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AIJS-Experts - Cybersecurity for Generative AI
JS-Experts - Cybersecurity for Generative AI
 
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
Optimizing Business Potential: A Guide to Outsourcing Engineering Services in...
 
Watermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security ChallengesWatermarking in Source Code: Applications and Security Challenges
Watermarking in Source Code: Applications and Security Challenges
 
ERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptxERP For Electrical and Electronics manufecturing.pptx
ERP For Electrical and Electronics manufecturing.pptx
 
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software TeamsYour Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
Your Vision, Our Expertise: TECUNIQUE's Tailored Software Teams
 
Why Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdfWhy Choose Brain Inventory For Ecommerce Development.pdf
Why Choose Brain Inventory For Ecommerce Development.pdf
 
Kawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in TrivandrumKawika Technologies pvt ltd Software Development Company in Trivandrum
Kawika Technologies pvt ltd Software Development Company in Trivandrum
 
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdfARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
ARM Talk @ Rejekts - Will ARM be the new Mainstream in our Data Centers_.pdf
 
Cybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and BadCybersecurity Challenges with Generative AI - for Good and Bad
Cybersecurity Challenges with Generative AI - for Good and Bad
 
Generative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-CouncilGenerative AI for Cybersecurity - EC-Council
Generative AI for Cybersecurity - EC-Council
 
Deep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - DatacampDeep Learning for Images with PyTorch - Datacamp
Deep Learning for Images with PyTorch - Datacamp
 
Growing Oxen: channel operators and retries
Growing Oxen: channel operators and retriesGrowing Oxen: channel operators and retries
Growing Oxen: channel operators and retries
 
online pdf editor software solutions.pdf
online pdf editor software solutions.pdfonline pdf editor software solutions.pdf
online pdf editor software solutions.pdf
 
Top Software Development Trends in 2024
Top Software Development Trends in  2024Top Software Development Trends in  2024
Top Software Development Trends in 2024
 
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine HarmonyLeveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
Leveraging DxSherpa's Generative AI Services to Unlock Human-Machine Harmony
 

From Business Idea to Successful Delivery by Serhiy Haziyev & Olha Hrytsay, SoftServe

  • 1. April, 2014 From Business Idea to Successful Delivery
  • 2. ▪ Leading global Product and Application Development partner founded in 1993 ▪ 3,200 employees across North America, Ukraine, Russia and Western Europe ▪ Thousands of successful outsourcing projects! Clients include: SaaS/Cloud Solutions . Mobility Solutions . UX/UI BI/Analytics/Big Data . Software Architecture . Security
  • 4. Big Data Successful Ideas Top 10 startups with most funding Big Data will drive $232 billion in IT spending through 2016 (Gartner) BIG DATA INVESTMENTS 2008-2012 $ 4.9B 2013 Jan-Oct $ 3.6B Source: www.bigdata-startups.com Big Data Use Cases Spotify – Changing the music industry Heineken – Shopperception analyses
  • 5. From In-house To Open Innovation Product development lifecycle 1. Envisioning & birth of idea 2. Probing user needs 3. Ideation & design 4. Conceptualization 5. Feasibility study 6. Strategizing 7. Business analysis 8. PoC, Prototyping 9. Final design 10. Technical implementation 11. Pilot production 12. Commercialization 13. Pricing 14. Maintenance & support 15. Extension thru innovation 16. Next version, goto 1 17. R.I.P. time span GENERICGENERICSPECIFIC process agility
  • 6. 1. Envisioning & birth of idea 2. Probing user needs 3. Ideation & design 4. Conceptualization 5. Feasibility study 6. Strategizing 7. Business analysis 8. PoC, Prototyping 9. Final design 10. Technical implementation 11. Pilot production 12. Commercialization 13. Pricing 14. Maintenance & support 15. Extension thru innovation 16. Next version, goto 1 17. R.I.P. From In-house To Open Innovation Product development lifecycle time span GENERICGENERICSPECIFIC process agility
  • 7. Reference Architectures: ▪ Extended Relational ▪ Non-Relational ▪ Hybrid Big Data Analytics Reference Architectures Architecture Drivers: ▪ Volume ▪ Sources ▪ Throughput ▪ Latency ▪ Extensibility ▪ Data Quality ▪ Reliability ▪ Security ▪ Self-Service ▪ Cost
  • 8. Relational Reference Architecture Web Services Mobile Devices Native Desktop Web Browsers Advanced Analytics OLAP Cubes Query & Reporting Staging Areas Operational Data Stores Data Marts Data Warehouses Replication API/ODBC Messaging ETL Unstructured Semi- Structured Data Sources Integration Data Storages Analytics Presentation Structured
  • 9. Extended Relational Reference Architecture Web Services Mobile Devices Native Desktop Web Browsers Advanced Analytics OLAP Cubes Query & Reporting Staging Areas Operational Data Stores Data Marts Data Warehouses Replication API/ODBC Messaging ETL Unstructured Semi- Structured Data Sources Integration Data Storages Analytics Presentation Structured
  • 10. Non-Relational Reference Architecture Web Services Mobile Devices Native Desktop Web Browsers Advanced Analytics Map Reduce Query & Reporting Search Engines Distributed File Systems NoSQL Databases API Messaging ETL Unstructured Semi- Structured Data Sources Integration Data Storages Analytics Presentation Structured
  • 11. Hybrid Reference Architecture Data Refinery Lambda Architecture Source:Source:
  • 12. Relational vs. Non- Relational Architecture Requirements Relational Non- Relational Comments Big Data Scalability Using the MPP techniques, relational data warehouses can run terabytes and even petabyte+ data. NoSQL solutions can scale further keeping dozens of petabytes. Ad-Hoc Reporting There is a variety of mature SQL BI tools in the market. At the same time, BI tools and connectors for NoSQL databases continue evolving. Near-Real Time Data Latency Although near-real time is achievable in both relational data warehouse and NoSQL solutions, it requires extra efforts optimizing data update and processing. Processing Raw Unstructured Data The benefit of NoSQL solutions (including the Hadoop ecosystem) is easy storing and processing of unstructured data. High Data Model Extensibility NoSQL schema-less nature allows easy extending of the data model with the new data attributes on the fly. Extending relational storages is still possible with design patterns, but it is quite limited and difficult if compared to NoSQL. Reliability and Fault-Tolerance Most of the relational and NoSQL technologies offer reliable solutions replicating data between redundant instances and supporting failover. High Data Quality and Consistency The RDBMS solutions are about the ACID concept, while NoSQL are about the BASE concept. This way, most of NoSQL solutions sacrifice consistency over availability, limiting their usage in quality critical applications. High Security and Regulatory Compliance Relational storages are traditionally strong in security as they offer role-based access, row-level security and encryption of data in transit and data in rest. Most of today’s NoSQL solutions have significant deficit of such features and rely on a perimeter security model. Low Cost There are at least two factors that make NoSQL solutions less costly than relational ones: 1) $0 or considerably low license fee due to open source 2) NoSQL databases typically use clusters of cheap commodity servers Skills Availability There is still an experience gap with NoSQL technologies on the IT labor market and the correspondent lack of in-house skills within organizations.
  • 13. Relational vs. Non- Relational Architecture Relational Non-Relational • Rational • Predictable • Traditional • Flexible • Agile • Modern
  • 14. Business Goals: Provide visual environment for building custom mobile application  Charge customers based on the platform they are using, number of consumers’ applications etc. Business Area: Cloud based platform for building, deploying, hosting and managing of mobile applications Case Study #1: Usage & Billing Analysis
  • 15. 1. Envisioning & birth of idea 2. Probing user needs 3. Ideation & design 4. Conceptualization 5. Feasibility study 6. Strategizing 7. Business analysis 8. PoC, Prototyping 9. Final design 10. Technical implementation 11. Pilot production 12. Commercialization 13. Pricing 14. Maintenance & support 15. Extension thru innovation 16. Next version, goto 1 17. R.I.P. SoftServe Involvement Product development lifecycle From In-house To Open Innovation with SoftServe
  • 16. Technologies:  Python  Amazon Redshift  Amazon SQS  Amazon S3  Elastic Beanstalk  Jaspersoft BI Professional  Aria Subscription Billing Platform ▪ Volume (> 10 TB) ▪ Sources (JSON) ▪ Throughput (> 10K/sec) ▪ Latency (2 min) ▪ Extensibility (Custom metrics) ▪ Data Quality (Consistency) ▪ Reliability (24/7) ▪ Security (Multitenancy) ▪ Self-Service (Ad-Hoc reports) ▪ Cost (The less the better ) ▪ Constraints (AWS) Architecture Drivers:
  • 17. Business Goals:  In addition to main service to build in-house Analytics Platform for ROI measurement and performance analysis of every product and feature delivered by the platform;  Platform should provide the ability to understand how end-users are interacting with service content, products, and features on sites;  Take ownership of analytics event stream;  Perform A/B Testing Business Area: Retail. A platform for e-commerce and collecting feedbacks from customers Case Study #2: Clickstream for retail website
  • 18. 1. Envisioning & birth of idea 2. Probing user needs 3. Ideation & design 4. Conceptualization 5. Feasibility study 6. Strategizing 7. Business analysis 8. PoC, Prototyping 9. Final design 10. Technical implementation 11. Pilot production 12. Commercialization 13. Pricing 14. Maintenance & support 15. Extension thru innovation 16. Next version, goto 1 17. R.I.P. SoftServe Involvement Product development lifecycle From In-house To Open Innovation with SoftServe
  • 19. Technologies: • Amazon Elastic Load Balancer, S3 • Tornado, Flume • Hadoop/HDFS, HBase, MapReduce, Oozie (Cloudera distribution) • Backbone ▪ Volume (45 TB) ▪ Sources (JSON) ▪ Throughput (> 10K/sec) ▪ Latency (1 hour) ▪ Extensibility (Custom tags) ▪ Data Quality (Not critical) ▪ Reliability (24/7) ▪ Security (Multitenancy) ▪ Self-Service (Canned reports) ▪ Cost (The less the better ) ▪ Constraints (AWS) Architecture Drivers:
  • 20.  Understand in-house Big Data capabilities  Determine what steps from Product development lifecycle can be done with partners  Establish collaboration approach  Do feasibility study  Create architecture and select technology stack  Do prototyping, re-evaluate architecture  Estimate implementation efforts  Align project milestones and success criteria  Align team structure and processes  Set up transparent knowledge management environment  Set up DevOps practices from the very beginning  Advance in Product development through “Small Wins” Checklist for Successful Delivery
  • 21. SoftServe UK Office Regent's Place, 338 Euston Road, London, NW1 3BT Tel: +44 203 519 1216 Contacts Serhiy Haziyev: shaziyev@softserveinc.com Olha Hrytsay: ohrytsay@softserveinc.com Glen Wilson: gwil@softserveinc.com Thank You!