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BIG DATA PLATFORM
INDUSTRIALIZATION

BIG DATA INITIAVES @Renault
2014
Big Data Sandbox on old
HPC Infrastructure.
Site: Innovation LAB
POC: Quality Data
Exploration
2015
DataLab Implementation
New HP Infrastructure
Data Protection: NO
1st Level of
Industrialization
2016
Big Data Platform
Industrialization to host
both Pocs and Projects in
Production.
Data Protection: YES

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Big Data Deployment Production Stakes
• One Hadoop cluster with a 24/7 always-on visibility of data(instead of siloing them).
• Many crossing Data possibilities
• Simplify Operations
• Design Simplicity
• Charge Back Model
• Scalability and Isolation
• Isolate Experimental applications from Production

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Déploiement des projets Qualité sur DataLake
Big Data
Developpers
Serveur Client
(Clients Hadoop
Installés)
DMZ
KNOX
GATEWAY
Search
Node
Edge
Node
Name
Node
DataStore
Master DNDNDNDNDNDNDNDNDN
DNDNDNDNDNDNDNDNDN
Data Sources
LoadBalancer
Web
Applications
Web service
Import
Access GUI
Search
Node

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Quality
Sales and
Marketing
Supply Chain Engineering
Consumers
Open Data
Internet of Things
Producers
Batch (RDBMS,
Files)
Messages, Logs
Streaming, Data Flow
NFS Gateway, Sqoop,
Spark SQL
FLUME
LOGSTASH
KAFKA PRODUCERS
Kafka
Broker
(Topics)
Spark
Streaming
Elasticsearch
HBASE
HIVE
HDFS
Spark SQL
Spark RDD
Big Data Ecosystem @ Renault
YARN + HDFS

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Data Ingestion Scenarios : RDBMS
RDBMS
Sqoop
Spark SQL
HIVE
Flat Files
HBase
Basic data import based on column id (Integer) or timestamp
Example: SOPHIA
ELT Architecture: Extract Load and Transform
Non standard Data Import, Specific schema
Example: BLMS
Support ETL Architecture : Extract Transfrom and Load
Support Ingestion directly to Elasticsearch
INSERT ONLY
NOCTURNAL BATCH
SQL QUERIES
HIGH LATENCY
INSERT ONLY
NOCTURNAL BATCH
DATA PROCESSING
Files Format: CSV,
PARQUET, AVRO
INSERT AND UPDATE
NOSQL DB (KEY-VALUE SCHEMA)
LOW LATENCY
FOR SCALING OUT RELATIONAL
DB ON HADOOP
INTERACTIVE ANALYTICS
(SPOTFIRE)
VERY LOW LATENCY (SSD DISKS)
NEAR REAL TIME ANALITYCS
(WATCHING ALERTS)
TEXT SEARCH (LOG ANALYSIS)
NESTED and PARENT/CHILD
RELATIONSHIPS
Elasticsearch

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Interactive SQL Data Analytics
Main Objectives
• Speeding up BI queries on Big Data Stores
• Hide Complexity of Big Data Architecture to End-User and Provide Only one Data
Connector for Spotfire
• Provide Interactive User Experience
• Data Virtualization (No need to import RDBMS systematically for Crossing Data)
 Spark SQL (1.6) : The emerging solution for Interactive SQL Data Analytics with the Data
Source API.

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Only One Data Connector
Data Processing Applications
Add In-Memory Capability
Load/Insert Load/Insert Load/Insert
Interactive SQL Data Analytics
HBASEHIVE Elasticsearch
Spark SQL
Files Parquet
DataSource API
Load/Insert
RDBMS
Load/Insert

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Big Data In Action
Multitenancy
High Availability
Security
Data Governance
Policy
Continuous Delivery
Data Protection
Hadoop
Organization

POC#
0
POC#
1
POC#
2
POC#
n
Release
Management
PRODUCTION
SLA & Monitoring
Tenant 1
App 1
Tenant 2
App 1
App 2
Tenant n
App 1
Managementand
Monitoring
DataLab, Open to
Data Exploration
Bi-modal Big Data Platform
One physical Cost –effective platform

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Hadoop Global Data Life Cycle
Data Sources
Load or archive
batch data
Stream real time
data
Mask Sensitive data
with Automated
Process
Renault Big Data
Platform
Refine, curate, process,
query data
Big Data Web
Services
INGESTION
Scheduled and
Monitored by
AITS in PROD
Policies pre-defined by
Security Officer
ADA
ARCA
subscription
request to
datasets
Data
Access
AITS: Groups
Management
DIRx Data
Owner
Validation
Sync
Users
Defined in Ranger
and Protegrity
Defined in
Falcon

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Active / Active Hadoop Platform
Rack Salle 1 C2 Rack Salle 2 C2
Data Nodes
for Bloc Storage
HDFS PROD HDFS POC
High Availability Architecture

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Hadoop Security Levels
OS Security
Authorization
Perimeter Level Security
Protected Zone
Data Protection
Selected Solution :
Tokenization
(Protegrity)

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Tokenization Definition
Selected solution: Tokenization
• Tokenization is a form of data protection that
converts sensitive data into fake data.
• The real data can be retrieved by authorized
users.
• Protegrity: The only Available Solution for Hadoop
(supports also traditional Data Systems)
Data Protection Key Requirements:
• Ability to de-identify Personally Identifiable Data
• Restrict data access (financial data, Data
residency obligations, …)
• Provide central management and control of all
data security operations

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Identifier Clear Protected
Authorized Role 1
* Can see most data in the
clear
Authorized Role 2
* Can see limited data in the
clear
Name Joe Smith csu wusoj Joe Smith Joe Smith
Address 100 Main Street, Pleasantville,
CA
476 srta coetse, cysieondusbak,
HA
100 Main Street, Pleasantville,
CA
“No Access”
Date of Birth 12/25/1966 01/02/1966 12/25/1966 01/02/1966
VIN VF1112C0000724284 AB9875R8467364752 VF1112C0000724284 “No Access”
Credit Card
Number
3678 2289 3907 3378 3846 2290 3371 3890 xxxx xxxx xxxx 3378 3846 2290 3371 3890
E-mail Address joe.smith@surferdude.org eoe.nwuer@beusorpdqo.aku joe.smith@surferdude.org joe.smith@surferdude.org
Telephone
Number
760-278-3389 998-389-2289 760-278-3389 998-389-2289
DATA PROTECTION Example 1
Fine Grained Protection

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DATA PROTECTION Example 2
Data Residency

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The Next Step: HDFS FEDERATION
Name Service 1
/store1/
Name Service 2
/store2/
Name
Node 1
Name
Node 2
Name
Node 3
Name
Node 4
Data
Node 1
Data
Node 2
Data
Node 3
Data
Node 4
Data
Node 5
Data
Node 6
Data
Node 7
Data
Node 8
Federation
Scale-out Data Nodes
Resources usage orchestrated by YARN
HA HA
• see only access /store2
• cannot access to /store1
• cannot access to
Name Node 1 and 2
• see only access /store1
• cannot access to /store2
• cannot access to
Name Node 3 and 4
PHYSICALISOLATION
Privileged Users can
access /store1 and /store2
for Crossing Data
Bloc Storage
Unreadable Data
Data
Node 9

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BIG DATA PROJECT PROCESS
Business
Use Case DIRx
POC PROD Project
Data
Exploration
AT
Data Sources Ingestion
Security Policies
Data Processing development
User Access Authorization
CPT
DIA - Innovation Front End deployment
Admin
@BICC
architecture
development
MEP
Implementation
Deployment
management
DAT

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DATA CHANGE MANAGEMENT
• Publish real time dashboard of all data flows.
• For each data source the producer and the consumers will be displayed:
• Producer: the DIRx generating the Data
• Consumers: all the Big Data applications using this Data source
• If the Producer decides to change the data source schema, He has to send notification
from the dashboard to all consumers.
• By monitoring in real time the logs of data ingestion jobs, the failure detection is more
reactive and efficient.

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COLLECT LOGS FOR (NRT) CARTO
Sqoop
Metastore
Storing Jobs
Hive
Metastore
Storing SQL Schema
Oozie
Metastore
Storing
Workflows
Oozie Logs
Yarn
Job History Logs
Storing
Projects
Hue
Database
Elasticsearch
J
D
B
C
P
L
U
G
I
N
L
O
G
S
T
A
S
H
Kibana

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Dynamic Relationship Portal