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

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

GEN

ERIC

G

ENER

IC

SPEC

IFIC

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

GEN

ERIC

G

ENER

IC

SPEC

IFIC

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: [email protected]

Olha Hrytsay: [email protected]

Glen Wilson: [email protected]

Thank You!