1. Building your own Data Science
platform in the cloud
GUR FlautR – Paris, November 14th 2012
2. Who Am I
• Co-founder and Data Scientist at Dataiku
• Long-time data hacker
– Telco (Orange)
– Retail (Catalina Marketing, all major French retailers)
– High Tech (Apple)
– Social Gaming (Is Cool Entertainment)
– Data Provider (qunb)
• I love data and blending innovative technologies and methods
to get the most out of a dataset.
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3. Agenda
• Introducing Dataiku
• Motivations & building blocks
• Setting up the Data Science stack
• Annexes (with step-by-step tutorial)
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5. Product Innovation
opposes conflicting views
User Experience?
Product
Features?
Designer
Roadmap?
Satisfaction? Business Acquisition? Pricing?
New
Perception? User Voice Product ?
& Loyalty?
Engagement? Marketing
Planning?
Performance? Engineers Today, Innovation requires
Reliability? to put together different expertise
and different views…
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6. Data Innovation: fill the gap!
User Feedback (A/B Test)
Product
Continuous improvement
Designer
Personalized Business Targeted campaings
experience User Voice Data ! & Price optimization
Marketing
Quality Assurance
Workload and yield Engineers A common ground to
management federate your product teams
towards a common goal
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7. An exploratory and iterative approach…
• You can’t « design »
Generate Select &
Ideas Develop
insights, you explore
and discover them…
Form
Function • Iterate quickly with
constant feedback
Explore and Experience
Experiment
Refine Surprise
• Try a lot, don’t be
Emotion afraid to fail!
Culture
Enhance or Gather
Discard Feedback
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8. …which is key to your future business
models
• Personalized • Detailed Risk • Personalized
Subscription Models Analytics Models Treatment
Digital
Insurance Healthcare
Publishing
• Optimized Traffic • Bio Surveillance with • … to imagine !
Network captors networks
Transportation Environment Your Business
?
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9. The « data lab »
• data lab, (n. m): a small group with
all the expertise, including business
minded people, machine learning
knowledge and the right technology
• A proven organization used by
successful data-driven companies
over the past few years
(eBay, LinkedIn, Walmart…)
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10. How does it work?
Real Lab Data Lab
Tools Software and Servers
• To perform experiment • Store, process, analyze
Protocols Intelligence
• How to apply experiment • Models, Algorithms
People People
• Scientists • Data Scientists
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11. But it’s not so easy…
• Lot of recent open source
Technologies technologies to choose from
• Complex integration and usage
• Very rare skills
People
• Hard to recruit or train
Data Lab
• Lack of integrated teams
Governance
• New mindset to adopt
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12. Our mission
Dataiku help you find your path to
‟ Data-Driven Innovation,
building (or accelerating) your own lab
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” 12
13. Dataiku
Your data lab accelerator
Dataiku Platform
•Ready-to use platform to store, process and analyze your data
•Open Source Technologies
•Machine learning + statistics + distributed computing
•Scale from 10GB to 1PTB
Dataiku Innovation
•Dedicated programs to kick start data science practice in your
company
•Assess your Data potential
•Bootstrap your Data Science practices
•Build a fully integrated Data Science team in your org
Dataiku Community
• A community of data science experts that help you
grow your organization to Data Science
• Unique Data Scientist training Program
• Network of experts that can be activated “as a
service”
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14. A Data Science Platform
MOTIVATIONS & BUILDING BLOCKS
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15. Motivations
• I often face situations where I need a lot of flexibility and
computing resources to address my day-to-day work, while
being on a budget.
• There are a lot of (new, and often open source) technologies
out there to deal with data, but sometimes poor
documentation make them hard to use.
• To address this issue, I am going to detail the set up of a data
science platform with some of these technologies.
– There are a lot of other options of course, but this one proved to work
very well.
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16. A new framework to process data
• Cloud Computing offers a new paradigm vs. computation
power and flexibility
– Ideal when a lot of processing power is required temporarily (think, a
lot of RAM for R…)
– When building a prototype or when you don’t have internal resources
available
• Open Source brings in best-of-breed technologies and
analytical capabilities
• Together, they allow to experiment in a whole new way with
data.
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17. The building blocks
Fast data storage Cutting-edge
and querying system analytics engine
Infrastructure
• it is flexible and cost effective
• it allows to experiment and iterate fast
• it can be extended easily with other
components, such as Hadoop (via EMR or
CDH)
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18. Infrastructure
• Amazon Web Services is one of the leading cloud computing provider.
• It is IAAS (infrastructure as a service), which means it offers all the required
components but you’ll need to configure and assemble them together.
• The components we are interested in today:
– EC2 (Elastic Cloud Compute) : servers
– EBS (Elastic Block Storage) : data persistence
– S3 : file system
• Be warned, this type of service is good for experimenting and for temporarily
resource needs. The cost could grow quickly if you use it on a regular basis.
• See current price lists in the addendum.
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19. Data Storage and Querying
• Vertica is a very fast, column-oriented database, specialized in analytical workloads (large
scans / joins / aggregations).
• It offers fast data loading, is SQL-99 compliant (“analytical” queries), and can be extended
using User-Defined Functions, including R.
• Vertica is not an open source technology, but provides with a Community Edition, for free
– Paid version is massively parallel (scale out architecture) among other things
– Community Edition could use up to 3 nodes
• There are a few other options in this space, open source or not:
– InfiniDB / Infobright (MySQL based, less practical “analytical” wise)
– Greenplum, Aster Data
– Netezza, Teradata, Oracle Exadata…
– “Big Data” alternatives: Cloudera’s Impala (relying on Hive), the incubating Apache Drill
(open source version of Google’s Dremel’s, accessible today via Google Big Query)
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20. Analytical Engine
• Well, I guess you all know it…
• We’ll be using R Studio here, in Server version
– Access the IDE in a web browser
– Has a lot of nice features, like Git integration, the “Shiny”
project…
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21. SETTING UP THE DATA SCIENCE
STACK
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22. Preamble
• This is not as easy as it sounds
• It is a bit techy, and some optimizations in the following
process might exist.
• The very detailed step-by-step tutorial can be found in the
addendum part of this deck, or at
http://dataiku.com/blog/setting-up-a-cool-data-science-platform-
for-cheap/
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23. Requirements
• Create an Amazon Web Services at
– http://aws.amazon.com/fr/
– Payment info required if your organization does not have an account
yet, but it’s worth it
• Register for the Vertica Community Edition at
– http://my.vertica.com/
– Free, but might take a few days before your registration is approved
• Make sure you have a terminal client available (like iTerm on
Mac OS X or Putty on Windows)
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24. Schematic Steps
Launch an EC2 instance The “server” itself
Additional and persistent
Attach an EBS disk storage for the server
Install and Configure R Studio
Install Vertica Community Edition
Configure ODBC connectivity to Vertica CE
H.A.V.E F.U.N
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25. Creating the EC2 instance
Connect to the EC2 Create a key pair if not
management console Select “Launch Instance”
done already
• Store in a “safe” location on your
PC
Give a name to your Choose your instance type
Select a RHEL 6 “AMI”
instance and region
• If you have several • I used a “m3.xlarge” to start, but • OS must be compatible both with
instance, will be easier to can be resized later ! RStudio and Vertica (I used AMI
find later ami-41d00528)
Select your key pair Specify your security group Launch and wait
• That will be used to connect • Only TCP port 22 needs to be • Can take a few minutes
(“ssh”) to the server later opened (for ssh)
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26. Attach an EBS disk
Click on “Create Volume” Under “More..”, attach the
tab Specify a size and region
EBS to your instance
• Same region as your instance
• Size can be up to 1 Tb
Connect to the remote
Create a “mount point” Format your EBS
server
• mkdir –p /data • fdisk –l to list your devices • ssh –i /path/to/your/keypair
• mkfs –t ext3 /dev/your-ebs root@instance-public-dns
Mount the EBS on this
Test if everything is working
directory
• mount /dev/your-ebs /data • df –kh for example
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27. Install RStudio
Update your Yum package
manager with EPEL Install R Download RStudio Server
• To be able to yum install R • R base is required to make
RStudio work
Exit and log back using ssh
Create a dedicated user Install RStudio Server
port forwarding
Point your browser to You run RStudio in the
localhost:8787 Cloud
• You’ll work transparently from • That’s great !
your PC
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28. Install Vertica
Upload or download the Prepare the data directory
Vertica installer Run the installer
on the EBS
• The installer you got from • Where Vertica is going to store its • Don’t forget to point the
my.vertica.com data data directory to the EBS !
Log as dbadmin and run the
Exit adminTools Create a new database
adminTools tool
• The Vertica main account and
management tool
Test your new DB using the
“vsql” client
• Talk to Vertica as you would with
Postgres
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29. Configure ODBC connectivity to
Vertica
Install RODBC package Create the odbc.ini file Create the vertica.ini file
• Via yum install • ODBC driver configuration file
Check your connectivity Export VERTICAINI
• In RStudio • The system variable
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30. And now you can play !
Collect some weather data Create a Vertica table Load into Vertica
Analyze ! Put data into RStudio
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31. Thank You
Thomas Cabrol
thomas.cabrol@dataiku.com
+33 (0)7 86 42 62 81
@ThomasCabrol
http://dataiku.com
35. Connect to EC2 Management
console
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36. Under “Key Pairs”, create a new
key pair
Note: once created, you can reuse it at will
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37. Move your key pair to a safe
location
Set Read/Write permissions only on the key
Note: this is shown for Mac OS X.
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38. Click on “Launch Instance”
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39. Select the “Classic Wizard”
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55. Write down your public DNS
This will be used to connect
to the machine.
This will be re-affected each
time the instance is
stopped/started.
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56. Login to the machine
Start your favorite Terminal application.
Windows users could use Putty.
ssh : secured connection to a remote host
-i option is used to specify your key location
root is the base account used
@public-dns: this is why you need to remember your machine dns
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57. Find your EBS
The “fdisk” utility on RHEL with –l option could be used to locate the physical device where
your EBS is attached.
You’ll find one device with the size of your EBS approximately.
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58. Format your EBS (FIRST RUN
ONLY!)
At first use only of
your EBS, you’ll need to
format it using the
mkfs utility.
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59. Mount your EBS
This creates a “/data” directory first, then actually mounts the EBS to this point.
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60. Check that everything is okay
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61. Update your YUM repo
This is required to be able to install R (base)
from the Yum package manager
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66. Create a dedicated User
Creates a new sudo user called “rstudio”.
The “passwd” utility sets a new password
for it.
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67. Test your connection to RStudio
Close the current connection to the server
Re-issue a ssh connection, but this time a port forwarding option. All connections on the remote
8787 (Rstudio server) port will be channeled to the 8787 port of your local machine (better for
security)
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68. Install S3 tools
This step is not mandatory
but is used here because
the Vertica installer is
stored on S3.
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69. Configure S3 tools
Specify your Amazon
credentials: access key and
secret key (which can be
found under
https://portal.aws.amazon.
com/gp/aws/securityCrede
ntials)
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70. Download the Vertica installer
NOTE: this is specific to my installation, you must specify your own S3
bucket if you choose this way to store your Vertica installer.
Another option is to download the installer on your local machine, and
upload it back to the EC2 instance using a “scp” command.
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72. Prepare the data directory
This is where Vertica is going to persist its data. Make sure it has
permissions to write into it.
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73. Run Vertica installer
The “-d” option is very
important, this is how
to tell Vertica where to
store its data. We point
here to the directory
previously created on
the EBS.
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74. Change user and start adminTools
“dbadmin” is the account that handles Vertica management.
“adminTools” is the Vertica utility that can be used to actually configure and
execute the managements tasks (most of them could also be done directly via
the command line).
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