The document discusses the evolution and practical adoption of big data technologies, emphasizing their capabilities in processing and handling large scale datasets. It highlights real-world applications from various industries, and the importance of understanding data architecture for successful implementation. The text also touches on the future of big data with advancements such as SQL on Hadoop and the use of distributed computing frameworks.

1. Big Data in Practice:
A Pragmatic approach to Adoption and
Value creation
Raj Nair
Data Practitioner and Consultant

2. Application Services
• Enterprise Resource
Planning (ERP)
• eCommerce /
eBusiness
• Enterprise App Dev
and ECM
• Legacy Support,
Systems Integration
and Conversion
Info Management
• Business Intelligence
and Analytics
• Dashboards,
Scorecards, Reporting
• MDM & Data
Modeling
• Data Marts, ODS,
ETL, Data Mining
IT Infrastructure
• IT Professional
Services
• Network
Administration &
Support
• dB Admin &
Maintenance
• Hosting and
Application Support
Process & Governance
• SDLC – Agile, TDD,
TFD Iterative
• Requirements
Analysis, PMP,
Change Management
and Automated QA
• Training & Knowledge
Transition and
Technical
Documentation

3. Content NOT FOR DISTRIBUTION: Property
of Raj Nair
Object Technology Solutions Inc. (OTSI) is a leading Information
Technology (IT) Services and Solutions company founded in 1999.
Clientele of Fortune 500 companies providing IT Solutions in the areas
of SDLC, Information Management, Business Intelligence, ERP,
eCommerce (B2B, B2C), Mobile, Enterprise Solutions, Middleware and
Infrastructure.
Technology Expertise and Experience
SAP - Business Objects, ERP, Microsoft - SharePoint, .Net, SQL Server,
Project Server, IBM - WebSphere, Cognos, Rational Suite, HP - Testing
tools, PPM
Data - Oracle, DB2, SQLServer, Teradata, OS – Windows, Unix (AIX, Linux,
HP-UX) etc., Open Source, Java
Certified Diversity Supplier in KS, MO and IL

4. 1Big Data – The Original Use Case
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

5. An Open Source Engine
The Year was 2002 ….
Doug Cutting Mike Caferella

6. Already Somebody’s Biz Problem
• Problem of Capacity & Scale
http://

7. The Perfect Storm
MapReduce Google File System
BigTable

8. MapReduce
Google File System
+
=

9. 1Big Data – The Original Use Case
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

10. Yes, But… We are not Google
Sears: Dynamic
Pricing
AT&T, quantifying
customer impact from
failed cell towers
Nokia: Holistic view of how
users interact with apps
across the world
Zions Bancorp:
Analyze 130 data
sources for fraud Cerner:
Detecting Health
Risks

11. Every Day Big Data
Reaching scale-up limits on your server
Represents tools, technologies, frameworks
for storage and processing at scale
Represents Opportunity

12. Every Day Big Data
Reaching scale-up limits on your server
Represents tools, technologies, frameworks
for storage and processing at scale
Represents Opportunity

13. Every Day Big Data
Reaching scale-up limits on your server
Represents tools, technologies, frameworks
for storage and processing at scale
Represents Opportunity

14. Big Data 1.0 – The Hadoop Ecosystem
Software library
Framework for large scale distributed processing
Ability to scale to thousands of computers

15. Design Principles
- Large Data Sets
Classic Hadoop MapReduce – Batch Processing
- Moving computation is cheaper than
moving data
- Hardware Failure, redundancy

16. This not “That”
Is Is Not
A Software Framework
(Storage/Compute)
A Database Management System
An appliance
Batch Processing For real-time or interaction
Write Once, Read Many Delete and Update or “ACID”
Unassuming of data formats Imposing any schemas
Open Source Lock In
Made for commodity servers
with local disks
Meant to be run in virtualized
environments

17. What is this you call data?
Unlearn current notion of “Data”
Native Data Source

18. HDFS
Storage and Archival
MapReduce
Programming Library
Crunch
Data Pipeline
processing HBase
Real time access
(low latency)
Pig
M/R Abstraction
Hive
Data Warehouse
Sqoop
Data Transfer
Flume
Data Streaming
(High
Latency)
Data Processing Workload Management
Data Movement

19. Purpose Use it for
HDFS Distributed Storage Raw data storage and archival
Flume Data Movement Continuous Streaming into HDFS
Sqoop Data Movement Data transfer from RDBMS to
HDFS/HBase
HBase Workload Mgmt Near real-time read/write access to
large data sets
Hive Workload Mgmt Analytical queries; data warehouse
Map
Reduce
Data Processing Low level custom code for data
processing
Crunch Data Processing (Java) Coding M/R pipelines, aggregations
Pig Data Processing Scripting language; similar to Crunch

20. A Powerful Paradigm
Storage Layer
Query
Engine
Processing
Engine
Metadata
Hadoop – Separate Layers
Multiple Query Engines
Data in Native format
Oracle SQL Server
Storage
Query
Storage
Query
Storage
Query
DB2
Tightly integrated Proprietary
Stacks, cannot free your data

21. 1Big Data – The Original Use Case
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

22. Opportunity…
Transform Data Processing
Exploration
Information Enrichment
Data Archival

23. Data Processing Pipeline
Several sources
Varying Frequencies
Varying Formats
Quality check
Validations, Scrubbing
Transformations/Rules
Prune app data sources
Discard/Archive

24. Data Processing
Engine
Data Warehouse
Data
Storage

25. ETL Engine
Data Warehouse
Data
Storage

26. ELT
Data Warehouse
Data
Storage

27. From Source to Business Value
Shoe-horning
Relational fit
Loading
Archiving /
Purging
Biz Rules
Validations
Scrubbing
Mapping
Transforms
Staging Distribution
Prep
Tuning
Data stores
Minutes/Hours
Subset of Data
Hours
Reliability
Sourcing
Missed SLAs = Biz Frustration

28. From Source to Business Value
Significantly more
data sources
Highly scalable,
significantly performant
data processing
New business value,
Faster time to value

29. Data Exploration
Large reservoir of data
Descriptive Statistics
Central Tendencies
Dispersion
Visualization
Surprise Me!

30. Data Exploration
Courtesy: Data Science Central
http://www.datasciencecentral.com/profiles/blogs/r-hadoop-data-analytics-heaven

31. Information Enrichment

32. Information Enrichment

33. Data Archival
Recycle Policy

34. Data Archival
Storage in Native Format
Redundancy , Replication
Easily accessible, inexpensive

35. 1Big Data – The Original Use Case
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

36. Practical Adoption
Big Data Technologies don’t solve all
problems
Leveraging existing investments
Complexities of existing systems

37. Proof of Concept
Use your own data – realistic results
Focus on very specific pain points
Know what you are going to measure

38. Opportunity Identification
Shoe-horning
Relational fit
Loading
Archiving /
Purging
Biz Rules
Validations
Scrubbing
Mapping
Staging Distribution
Prep
Tuning
Data stores
Minutes/Hours
Subset of Data
Hours
Reliability
Sourcing

39. Data Processing
Engine
Data Warehouse
Data
Storage

40. Data Processing
Engine
Data Warehouse
Data
Storage
Keep all your raw data
Cheaper Hardware
Low cost per byte $$
High value per byte
Offload from RDBMS
Improve scale, performance
Leverage existing tools

41. Hardware on a budget
Master:
- 12 cores
- 32 GB RAM
- 2 TB SATA Drives, 7.2K RPM
Workers:
- 4 Nodes
- 12 cores
- 16 GB RAM
- 4 TB SATA Drives each, 7.2 PRM
$5000
$5000 each
4-Port 10 Gig Switch - $1500 Grand Total < $30,000
Software costs ? - 0

42. NoSQL
Data Processing
Engine
Data Warehouse
Data
Storage
Keep all your raw data
Cheaper Hardware
NoSQL
Low cost per byte $$
High value per byte

43. Exploratory BI / Analysis
Data
Storage
Makes Data exploration practically cheaper and faster
Use existing visualization tools (Tableau or other)
Check for integration with R

44. Data Architecture
• Single Important factor
• Don’t miss technology trends
But ….
It’s more about the battle plan

45. 1Big Data – The Road to Now
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

46. What about that RDBMS?
Too many new data types
Extreme demands for loading & query access
Dynamic / just in time schemas
SQL is great, but why limit to relational?
Still great for transactional workloads

47. What’s Next?
Multi-tenant
Hadoop
SQL on Hadoop
Security
In-memory Real
Time

48. HDFS 2
Storage and Archival
MapReduce
(BATCH)
HBase
(online)
Hive
(interactive)
YARN
Yet Another Resource Manager
In-memory Search
Application Container - scale resource management
Map Reduce becomes “one type of application workload”
Multi-tenant Hadoop

49. SQL on Hadoop
Impala
Tez
Phoenix
• Cloudera
• MPP Engine
• HortonWorks
• SQL on Hive
• Apache
• SQL on HBase

50. In memory and Real Time
Spark
Storm
Apache Drill
• 100x faster than
M/R
• Event processing
• Low latency ad
hoc queries
• Interactive
queries at scale

51. Honorable (Proprietary) mentions
RDBMS on Hadoop
Complete Package
MPP, SMP, DataFlow
HortonWorks underneath
Manage, Analyze
machine generated
data

52. 1Big Data – The Road to Now
2Mainstream Big Data
3Real World Use Cases and Applications
4Practical Adoption : Opportunity Identification
5Big Data 2.0 – What’s on the Horizon ?
6Conclusion

53. Where can I get Hadoop?
Distributors
Open Source Apache Project
And these guys…
Cloud

54. Conclusion
The Power & Paradigm of Distributed Computing
“Nativity” of Data – Unlearn old notions
Identify, understand your data processing pipeline
POC with a measurable, specific use case
Data Architecture – key to sustainable scalability
Stay informed