If data were equated to temperature in physical analogy, according to the second law of thermodynamics its accumulation would lead to the heat death of the universe. In fact, 90% of the world’s data has been generated during the last two years. Fortunately, business intelligence (BI) architectures and technologies are evolving rapidly to keep up with the exponential rise of data and project complexity. An almost 30-year era of dominance for the relational database management system (RDBMS) ended, and there is no one-size-fits-all solution that solves all of today’s Big Data challenges. BI architecture drivers must change to satisfy new requirements in format, volume, latency, hosting, analysis, reporting, and visualization. In this session, we expose a number of reference architectures that address these challenges and speed up the design and implementation process, making it more predictable and economical: - Traditional architecture based on an RDMBS data warehouse but modernized with column-based storage to handle a high load and capacity - NoSQL-based architectures that address Big Data batch and stream-based processing and use popular NoSQL and complex event-processing solutions - Hybrid architecture that combines traditional and NoSQL approaches to achieve completeness that would not be possible with either alone The architectures are accompanied by real-life projects and case studies that the presenters have performed for multiple companies, including Fortune 100 and start-ups.

1. BIG DATA ANALYTICS
REFERENCE ARCHITECTURES AND
CASE STUDIES
BY SERHIY HAZIYEV AND OLHA HRYTSAY

2. Agenda
2
Big Data
Challenges
Big Data
Reference
Architectures
Case
Studies
10 tips for
Designing
Big Data
Solutions

3. Big Data Challenges
3
UNSTRUCTURED
STRUCTURED
HIGH
MEDIUM
LOW
Archives Docs Business
Apps
Media Social
Networks
Public
Web
Data
Storages
Machine
Log Data
Sensor
Data
Data Storages
RDBMS, NoSQL, Hadoop, file systems
etc.
Machine Log Data
Application logs, event logs, server
data, CDRs, clickstream data etc.
Sensor Data
Smart electric meters, medical
devices, car sensors, road cameras
etc.
Archives
Scanned documents, statements,
medical records, e-mails etc..
Docs
XLS, PDF, CSV, HTML, JSON etc.
Business Apps
CRM, ERP systems, HR, project
management etc.
Social Networks
Twitter, Facebook, Google+,
LinkedIn etc.
Public Web
Wikipedia, news, weather, public
finance etc
Media
Images, video, audio etc.
Velocity Variety VolumeComplexity

4. Big Data Analytics
4
Traditional Analytics (BI) Big Data Analytics
Focus on
Data Sets
Supports
• Descriptive analytics
• Diagnosis analytics
• Limited data sets
• Cleansed data
• Simple models
• Large scale data sets
• More types of data
• Raw data
• Complex data models
• Predictive analytics
• Data Science
Causation: what happened,
and why?
Correlation: new insight
More accurate answers
vs

5. Big Data Analytics Use Cases
5
Data
Discovery
Business
Reporting
Real Time
Intelligence
Data Quality
Self Service
Business Users
Intelligent AgentsConsumers
Low Latency
Reliability
Volume
Performance
Data Scientists/
Analysts

6. Big Data Analytics Reference Architectures
6
Architecture Drivers: Reference Architectures:
▪ Volume
▪ Sources
▪ Throughput
▪ Latency
▪ Extensibility
▪ Data Quality
▪ Reliability
▪ Security
▪ Self-Service
▪ Cost
▪ Extended Relational
▪ Non-Relational
▪ Hybrid

7. Relational Reference Architecture
7
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Advanced
Analytics
OLAP Cubes
Query &
Reporting
Operational
Data Stores
Data Marts
Data
Warehouses
Replication
API/ODBC
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured

8. 8
Extended Relational
Reference Architecture
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Advanced
Analytics
OLAP Cubes
Query &
Reporting
Operational
Data Stores
Data Marts
Data
Warehouses
Replication
API/ODBC
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured
Key components affected with Big Data challenges

9. Non-Relational Reference Architecture
9
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
Key components introduced with non-relational movement

10. Extended Relational vs. Non-Relational Architecture
10
Architecture Drivers
Extended
Relational
Non-Relational
Large data volume
Self-service (ad-hoc reporting)
Unstructured data processing
High data model extensibility
High data quality and consistency
Extensive security
Reliability and fault-tolerance
Low latency (near-real time)
Low cost
Skills availability

11. Extended Relational vs. Non-Relational Architecture
11
Architecture Drivers
Extended
Relational
Non-Relational
Large data volume
Self-service (ad-hoc reporting)
Unstructured data processing
High data model extensibility
High data quality and consistency
Extensive security
Reliability and fault-tolerance
Low latency (near-real time)
Low cost
Skills availability

12. Relational vs. Non-Relational Architecture
12
Relational Non-Relational
• Rational
• Predictable
• Traditional
• Agile
• Flexible
• Modern

13. Data
Discovery
Business
Reporting
Real Time
Intelligence
Big Data Analytics Use Cases
13
Business Users
Intelligent AgentsConsumers
Performance
Volume
Data Scientists

14. Data Discovery: Non-Relational Architecture
14
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

15. Data
Discovery
Business
Reporting
Real Time
Intelligence
Big Data Analytics Use Cases
15
Intelligent AgentsConsumers
Data Scientists
Data Quality
Self Service
Business Users

16. Business Reporting: Hybrid Architecture
16
Web Services
Mobile
Devices
Native
Desktop
Web
Browsers
Map Reduce
SQL Query &
Reporting
Distributed File
Systems
API
Messaging
ETL
Unstructured
Semi-
Structured
Data Sources Integration Data Storages Analytics Presentation
Structured
Relational
DWH/DM
Advanced
Analytics
Search Engines
Extended Relational components Non-relational components

17. Data
Discovery
Business
Reporting
Real Time
Intelligence
Big Data Analytics Use Cases
17
Data Scientists Business Users
Intelligent AgentsConsumers
Low Latency
Reliability

18. Lambda Architecture
18
Source:

19. 19
Business Goals:
 Provide development environment
for building custom mobile applications
 Charge customers for the platform they
use with pay-as-you-go model
Business Area:
Cloud based platform for building, deploying,
hosting and managing mobile applications
Case Study #1: Usage & Billing Analysis

20. Architectural Decisions
20
▪ Volume (> 10 TB)
▪ Sources (Semi-structured - 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 (Public Cloud)
Architecture Drivers:
Trade-off:
//
Extended
Relational
Non-Relational
Extensibility - +
Data Quality + -
Self-Service + -
 Extended Relational Architecture
 Extensibility via Pre-allocated
Fields pattern

21. Solution Architecture
21
Technologies:
• Amazon Redshift
• Amazon SQS
• Amazon S3
• Elastic Beanstalk
• Jaspersoft BI Professional
• Python

22. 22
Business Goals:
 Build in-house Analytics Platform for ROI measurement
and performance analysis of every product and feature
delivered by the e-commerce platform;
 Provide the ability to understand how end-users are
interacting with service content, products, and features on
sites;
 Do clickstream analysis;
 Perform A/B Testing
Business Area:
Retail. A platform for e-commerce and
collecting feedbacks from customers
Case Study #2: Clickstream for retail website

23. //
Extended
Relational
Non-
Relational
Volume/Scalability +/- +
Throughput + +
Self-Service + +/-
Extensibility - +
Architectural Decisions
23
▪ Volume (45 TB)
▪ Sources (Semi-structured - JSON)
▪ Throughput (> 20K/sec)
▪ Latency (1 hour)
▪ Extensibility (Custom tags)
▪ Data Quality (Not critical)
▪ Reliability (24/7)
▪ Security (Multitenancy)
▪ Self-Service (Canned reports, Data
science)
▪ Cost (The less the better )
▪ Constraints (Public Cloud)
Architecture Drivers:
Trade-off:
 Non-Relational Architecture
 Reporting via Materialized View
pattern

24. Solution Architecture
24
Technologies:
• Amazon S3
• Flume
• Hadoop/HDFS, MapReduce
• HBase
• Oozie
• Hive
Node 1
Node 2
Node N

25. 10 Tips for Designing Big Data Solutions
25
 Understand data users and sources
 Discover architecture drivers
 Select proper reference architecture
 Do trade-off analysis, address cons
 Map reference architecture to technology stack
 Prototype, re-evaluate architecture
 Estimate implementation efforts
 Set up devops practices from the very beginning
 Advance in solution development through “small wins”
 Be ready for changes, big data technologies are evolving
rapidly

26. 26
▪ Leading global Product and
Application Development partner
founded in 1993
▪ 3,300+ employees across North
America, Ukraine and Western
Europe
▪ Thousands of successful outsourcing
projects!
SaaS/Cloud Solutions . Mobility Solutions . UX/UI
BI/Analytics/Big Data . Software Architecture . Security
Clients include:

27. Thank You!
27
SoftServe US Office
One Congress Plaza,
111 Congress Avenue, Suite 2700 Austin, TX
78701
Tel: 512.516.8880
Contacts
Serhiy Haziyev: shaziyev@softserveinc.com
Olha Hrytsay: ohrytsay@softserveinc.com