This document discusses enabling the lean enterprise through technologies like microservices, continuous integration/deployment, and cloud computing. It begins by defining the lean enterprise and the OODA loop concept. It then explains how technologies like AWS, big data, and microservices can help organizations continuously observe, orient, decide, and act. Specific AWS services like EC2, EMR, Kinesis, Redshift, S3, and DynamoDB are reviewed. The benefits of breaking up monolithic systems into microservices and implementing devops practices like CI/CD are also summarized.

1. How to enable the
Lean Enterprise
Johann Romefort
co-founder @ rainbow

2. My Background
• Seesmic - Co-founder & CTO
Video conversation platform
Social media clients…lots of pivots :)
• Rainbow - Co-founder & CTO
Enterprise App Store

3. Goal of this presentation
• Understand what is the Lean
Enterprise, how it relates to big data
and the software architecture you
build
• Have a basic understanding of the
technologies and tools involved

4. What is the Lean
Enterprise?
http://en.wikipedia.org/wiki/Lean_enterprise
“Lean enterprise is a
practice focused on
value creation for the
end customer with
minimal waste and
processes.”

5. Enabling the
OODA Loop
!
!
“Get inside your adversaries'
OODA loop to disorient them”
!
OBSERVE
ORIENT
DECIDE
ACT
USAF Colonel John Boyd on Combat:
OODA Loop

6. Enabling the
OODA Loop
OODA Loop

7. The OODA Loop
for software
image credit: Adrian Cockcroft

8. OODA Loop
• (Observe) Innovation and (Decide)
Culture are mainly human-based
• Orient (BigData) and Act (Cloud) can
be automated

9. ORIENT

10. What is Big Data?
• It’s data at the intersection of 3 V:
• Velocity (Batch / Real time / Streaming)
• Volume (Terabytes/Petabytes)
• Variety (structure/semi-structured/unstructured)

11. Why is everybody talking about it?
• Cost of generation of data has gone down
• By 2015, 3B people will be online, pushing data
volume created to 8 zettabytes
• More data = More insights = Better decisions
• Ease and cost of processing is falling thanks to
cloud platforms

12. Data flow and constraints
Generate
Ingest / Store
Process
Visualize / Share
The 3 V involve
heterogeneity and
make it hard to
achieve those steps

13. What is AWS?
• AWS is a cloud computing platform
• On-demand delivery of IT resources
• Pay-as-you-go pricing model

14. Cloud Computing
+ +
StorageCompute Networking
Adapts dynamically to ever
changing needs to stick closely
to user infrastructure and
applications requirements

15. How does AWS helps
with Big Data?
• Remove constraints on the ingesting, storing, and
processing layer and adapts closely to demands.
• Provides a collection of integrated tools to adapt to
the 3 V’s of Big Data
• Unlimited capacity of storage and processing power
fits well to changing data storage and analysis
requirements.

16. Computing Solutions
for Big Data on AWS
Kinesis
EC2 EMR
Redshift

17. Computing Solutions
for Big Data on AWS
EC2
All-purpose computing instances.
Dynamic Provisioning and resizing
Let you scale your infrastructure
at low cost
Use Case: Well suited for running custom or proprietary
application (ex: SAP Hana, Tableau…)

18. Computing Solutions
for Big Data on AWS
EMR
‘Hadoop in the cloud’
Adapt to complexity of the analysis
and volume of data to process
Use Case: Offline processing of very large volume of data,
possibly unstructured (Variety variable)

19. Computing Solutions
for Big Data on AWS
Kinesis
Stream Processing
Real-time data
Scale to adapt to the flow of
inbound data
Use Case: Complex Event Processing, click streams,
sensors data, computation over window of time

20. Computing Solutions
for Big Data on AWS
RedShift
Data Warehouse in the cloud
Scales to Petabytes
Supports SQL Querying
Start small for just $0.25/h
Use Case: BI Analysis, Use of ODBC/JDBC legacy software
to analyze or visualize data

21. Storage Solution
for Big Data on AWS
DynamoDB RedShift
S3 Glacier

22. Storage Solution
for Big Data on AWS
DynamoDB
NoSQL Database
Consistent
Low latency access
Column-base flexible
data model
Use Case: Offline processing of very large volume of data,
possibly unstructured (Variety variable)

23. Storage Solution
for Big Data on AWS
S3
Use Case: Backups and Disaster recovery, Media storage,
Storage for data analysis
Versatile storage system
Low-cost
Fast retrieving of data

24. Storage Solution
for Big Data on AWS
Glacier
Use Case: Storing raw logs of data. Storing media archives.
Magnetic tape replacement
Archive storage of cold data
Extremely low-cost
optimized for data infrequently
accessed

25. What makes AWS different
when it comes to big data?

26. Given the 3V’s a collection of tools is most of the time
needed for your data processing and storage.
Integrated Environment for Big Data
AWS Big Data solutions comes integrated with each others
already
AWS Big Data solutions also integrate with the whole AWS
ecosystem (Security, Identity Management, Logging, Backups,
Management Console…)

27. Example of products interacting with
each other.

28. Tightly integrated rich
environment of tools
On-demand scaling sticking to
processing requirements
+
=
Extremely cost-effective and easy to
deploy solution for big data needs

29. • Error Detection: Real-time detection of hardware
problems
• Optimization and Energy management
Use Case:
Real-time IOT Analytics
Gathering data in real time from sensors deployed in
factory and send them for immediate processing

30. First Version of the
infrastructure
Aggregate
Sensors
data
nodejs
stream
processor
On customer site
evaluate rules
over time
window
in-house hadoop cluster
mongodb
feed algorithm
write raw
data for
further
processing
backup

31. Version of the infrastructure
ported to AWS
Aggregate
Sensors
data
On customer site
evaluate rules
over time
window
write raw
data for
archiving
Kinesis RedShift
for BI
analysis
Glacier

32. ACT

33. Cloud and Lean
Enterprise

34. Let’s start with a
personal example

36. First year @seesmic
• Prototype becomes production
• Monolithic architecture
• No analytics/metrics
• Little monitoring
• Little automated testing

37. I built a monolith

38. or…at least I tried

39. Early days at SeesmicFirst year @seesmic

40. Everybody loves a good
horror story

41. We crashed Techcrunch

43. What did we do?

44. Add a QA Manager

45. Add bearded
SysAdmin

46. We added tons of process
so nothing can’t go wrong

47. Impact on dev team
• Frustration of slow release process
• Lots of back and forth due to bugs and
the necessity to test app all over each
time
• Chain of command too long
• Feeling no power in the process
• Low trust

48. Impact on product team
• Frustration of not executing fast
enough
• Frustration of having to ask for
everything (like metrics)
• Feeling engineers always have the last
word

49. Impact on Management

50. • Break down software into smaller
autonomous units
• Break down teams into smaller
autonomous units
• Automating and tooling, CI / CD
• Plan for the worst
What can you do?

51. =
Break down software into
smaller autonomous units

52. Introduction to
Microservices

53. Monolith vs Microservices
- 10000ft view -

54. Monolith vs Microservices
- databases -

55. Monolith vs Microservices
- servers -

56. Microservices
- example -

57. Break down team into
smaller units

58. Amazon’s
“two-pizza teams”
• 6 to 10 people; you can feed them
with two pizzas.
• It’s not about size, but about
accountability and autonomy
• Each team has its own fitness
function

59. • Full devops model: good tooling needed
• Still need to be designed for resiliency
• Harder to test
Friction points

60. Continuous Integration
(CI) is the practice, in software engineering, of
merging all developer working copies with a
shared mainline several times a day

61. Continuous Deployment

62. Continuous Deployment

63. Tools for Continuous
Integration
• Jenkins (Open Source, Lot of plugins,
hard to configure)
• Travis CI (Look better, less plugins)

64. Tools for Continuous
Deployment
• GO.cd (Open-Source)
• shippable.com (SaaS, Docker
support)
• Code Deploy (AWS)
+ Puppet, Chef, Ansible, Salt, Docker…

65. Impact on dev
• Autonomy
• Not afraid to try new things
• More confident in codebase
• Don’t have to linger around with old
bugs until there’s a release

66. Impact on product team
• Iterate faster on features
• Can make, bake and break hypothesis
faster
• Product gets improved incrementally
everyday

67. Impact on Management

68. • Enabling Microservices architecture
• Enabling better testing
• Enabling devops model
• Come talk to the Docker team
tomorrow!

69. Thank You
follow me: @romefort
romefort@gmail.com