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Discover 
Red 
Hat 
and 
Hortonworks 
for 
the 
Modern 
Data 
Architecture 
Kimberly 
Palko 
Product 
Manager 
Red 
Hat 
1 RED 
HAT 
JBOSS 
MIDDLEWARE
2 RED 
HAT 
JBOSS 
MIDDLEWARE 
Agenda 
● Red Hat and JBoss Middleware Overview 
● Combining data in Hadoop with traditional data 
sources 
● Federating two geographically distributed 
Hadoop clusters 
● Virtual data marts for Hadoop Lake
RED 
HAT 
& 
JBOSS 
MIDDLEWARE 
OVERVIEW 
3 RED 
HAT 
JBOSS 
MIDDLEWARE
Engineering 
CollaboraFon 
Benefits 
Integra<on 
with 
JBoss 
Data 
Virtualiza<on 
Enable 
agile 
Big 
Data 
Hadoop 
integra<on 
with 
exis<ng 
enterprise 
assets 
and 
maximize 
universal 
data 
u<liza<on 
to 
enable 
self-­‐service 
analy<cs 
4 RED 
HAT 
JBOSS 
MIDDLEWARE 
Integra<on 
with 
mul<ple 
Red 
Hat 
JBoss 
Middleware 
product 
family 
Enables 
millions 
of 
JBoss 
developers 
to 
quickly 
build 
applica<ons 
with 
Hadoop 
Integra<on 
with 
Red 
Hat 
Storage 
Enables 
Hadoop 
to 
use 
Red 
Hat 
Storage 
secure 
resilient 
storage 
pool 
for 
data 
applica<ons 
Integra<on 
with 
Red 
Hat 
Enterprise 
Linux 
OpenStack 
PlaOorm 
Simplifies 
automated 
deployment 
of 
Hadoop 
on 
OpenStack 
Integrated 
with 
Red 
Hat 
Enterprise 
Linux 
and 
OpenJDK 
Develop 
and 
deploy 
Apache 
Hadoop 
as 
an 
integrated 
component 
for 
mul<ple 
deployment 
scenarios
Big 
Data 
Integra<on: 
Turn 
Data 
into 
Ac<onable 
Informa<on 
Speed 
of 
Itera<on 
leads 
to 
Success 
Semi 
/ 
Unstructured 
Data 
5 RED 
SOCIAL, 
LOGS 
HAT 
JBOSS 
MIDDLEWARE 
Hadoop 
& 
NoSQL 
Data 
Integra<on 
& 
Data 
Services 
JBoss 
Data 
Virtualiza<on 
In-­‐memory 
data 
management 
JBoss 
Data 
Grid 
BI 
Analy<cs 
(diagnos<c, 
descrip<ve, 
predic<ve, 
prescrip<ve) 
SOA 
Applica<ons 
Event 
Processing 
& 
Messaging 
JBoss 
BRMS 
& 
JBoss 
A-­‐MQ 
Structured 
Data 
DW, 
OLAP, 
OLTP 
Streaming 
Data 
EVENTS, 
IOT 
Red 
Hat 
Enterprise 
Linux 
Red 
Hat 
Storage 
Analyze 
Integrate 
Enrich 
Ingest
Data 
Challenges 
Geang 
Bigger… 
HBase 
6 RED 
HAT 
JBOSS 
MIDDLEWARE 
NoSQL 
Hive 
MapReduce 
HDFS 
Storm 
Spark
Make 
Big 
Data 
Accessible 
for 
Everyone 
7 RED 
HAT 
JBOSS 
MIDDLEWARE
Data Supply and Integration Solution 
Data 
Virtualiza<on 
sits 
in 
front 
of 
mul<ple 
data 
sources 
and 
! allows 
them 
to 
be 
treated 
a 
single 
source 
8 RED 
HAT 
JBOSS 
MIDDLEWARE 
! delivering 
the 
desired 
data 
! in 
the 
required 
form 
! at 
the 
right 
<me 
! to 
any 
applica<on 
and/or 
user. 
THINK 
VIRTUAL 
MACHINE 
FOR 
DATA
Easy 
Access 
to 
Big 
Data 
Hive 
9 RED 
● Repor<ng 
tool 
accesses 
the 
data 
virtualiza<on 
server 
via 
HAT 
JBOSS 
MIDDLEWARE 
rich 
SQL 
dialect 
● The 
data 
virtualiza<on 
server 
translates 
rich 
SQL 
dialect 
to 
HiveQL 
● Hive 
translates 
HiveQL 
to 
MapReduce 
● MapReduce 
runs 
MR 
job 
on 
big 
data 
MapReduce 
HDFS 
Analytical 
Reporting 
Tool 
Data 
Virtualization 
Server 
Hadoop 
Big Data
Different 
Users 
Different 
Views 
of 
Big 
Data 
Hive 
10 RED 
● Logical 
tables 
with 
different 
forms 
of 
aggrega<on 
● Logical 
tables 
containing 
extra 
derived 
data 
● Logical 
tables 
with 
filtered 
data 
● All 
reports/users 
share 
the 
same 
specifica<ons 
HAT 
JBOSS 
MIDDLEWARE 
MapReduce 
HDFS
USE 
CASE 
1: 
COMBINING 
DATA 
FROM 
HADOOP 
WITH 
TRADITIONAL 
SOURCES 
-­‐ 
USING 
JBOSS 
DATA 
VIRTUALIZATION 
11 RED 
HAT 
JBOSS 
MIDDLEWARE
Integra<on 
of 
Big 
Data 
with 
“Small 
Data” 
12 RED 
HAT 
JBOSS 
MIDDLEWARE 
• Integra<ng 
small 
data 
with 
big 
data 
is 
easy 
• Integra<on 
specifica<ons 
can 
be 
shared 
or 
be 
developed 
for 
individual 
reports 
MapReduce 
HDFS 
Database 
Hive 
Applica<on 
Server
Hive 
13 RED 
HAT 
JBOSS 
MIDDLEWARE 
Caching 
the 
Big 
Data 
• Caches 
to 
speed 
up 
interac<ve 
repor<ng 
• Caches 
to 
create 
a 
consistent 
view 
of 
big 
data 
• Different 
caches 
for 
different 
reports 
MapReduce 
HDFS
14 RED 
HAT 
JBOSS 
MIDDLEWARE 
USE 
CASE 
2: 
GEOGRAPHICALLY 
DISTRIBUTED HADOOP 
CLUSTERS WITH DATA 
VIRTUALIZATION 
- SECURING DATA BY USER ROLE
Role based access control 
15 RED 
HAT 
JBOSS 
MIDDLEWARE 
Roles 
• Define 
roles 
based 
on 
organiza<on 
hierarchy 
Users 
• External 
authen<ca<on 
via 
Kerberos, 
LDAP, 
etc. 
VDB 
• Assign 
users 
and 
groups 
to 
a 
virtual 
data 
base
16 RED 
HAT 
JBOSS 
MIDDLEWARE 
Authentication 
Kerberos 
From 
client 
to 
the 
virtual 
data 
base 
Login 
Modules 
LDAP 
(MS 
Ac<ve 
Directory, 
OpenLDAP, 
etc.), 
any 
JAAS 
based 
security 
domain 
REST 
and 
Web 
Services 
WS-­‐UsernameToken 
HTTP 
Basic 
authen<ca<on 
SAML 
SAML 
authen<ca<on 
for 
web 
client 
applica<ons
Audit Logging via Dashboard 
17 RED 
HAT 
JBOSS 
MIDDLEWARE
Row 
and 
Column 
Masking 
18 RED 
-­‐ Row 
based 
masking 
Ex: 
keyed 
off 
geographic 
marker 
-­‐ 
Column 
masking 
to 
a 
constant, 
null, 
or 
a 
SQL 
statement 
Example: 
change 
all 
but 
the 
Last 
4 
digits 
in 
a 
credit 
card 
number 
to 
stars 
concat('****', 
substring(column, 
length(column)-­‐4)) 
HAT 
JBOSS 
MIDDLEWARE
Summary 
of 
Security 
Capabili<es 
● Authentication 
– Kerberos, LDAP, WS-UsernameToken, HTTP Basic, 
SAML 
19 RED 
HAT 
JBOSS 
MIDDLEWARE 
● Authorization 
– Virtual data views, Role based access control 
● Administration 
– Centralized management of VDB privileges 
● Audit 
– Centralized audit logging and dashboard 
● Protection 
– Row and column masking 
– SSL encryption (ODBC and JDBC)
Demonstration 
Geographically Distributed 
Hadoop Clusters with Data 
Virtualization - Securing 
Data by User Role 
20 RED 
HAT 
JBOSS 
MIDDLEWARE
Use Case 2: Federating across 
Geographically Distributed 
Hadoop Clusters 
Problem: 
Geographically distributed Hadoop 
clusters contains sensitive data like 
patient records or customer 
identification that cannot be 
accessed by other regions due to 
regulatory policy. IT needs access 
to all data, but users can only 
access the data in their region. 
21 RED 
HAT 
JBOSS 
MIDDLEWARE 
Solution: 
Leverage JBoss Data Virtualization to 
provide Row Level Security and 
Masking of columns while 
federating across Hadoop clusters. 
Data 
can 
be 
accessed 
by 
mulFple 
tools 
and 
methods 
already 
in-­‐house 
Consume 
Compose 
Connect 
JBoss 
Data 
Virtualiza<on 
Hiv 
e 
Hadoop 
cluster 
in 
one 
geographic 
region 
Hiv 
e 
Hadoop 
cluster 
in 
a 
second 
geographic 
region
Use Case 2 - Architecture 
APPLICATIONS 
22 RED 
HAT 
JBOSS 
MIDDLEWARE 
DATA 
SYSTEM 
Business 
AnalyFcs 
Custom 
ApplicaFons 
Packaged 
ApplicaFons 
VIRTUAL 
DATA 
MART
Use Case 2 - Resources 
23 RED 
HAT 
JBOSS 
MIDDLEWARE 
• GUIDE 
How 
to 
guide: 
https://github.com/DataVirtualizationByExample/ 
HortonworksUseCase2 
Tutorial: 
Available 
soon 
• VIDEOS: 
hpp://vimeo.com/user16928011/hortonworksusecase2short 
hpp://vimeo.com/user16928011/hortonworksusecase2short 
• SOURCE: 
hpps://github.com/DataVirtualiza<onByExample/HortonworksUseCase2
24 RED 
HAT 
JBOSS 
MIDDLEWARE 
USE 
CASE 
3: 
VIRTUAL DATA 
MARTS FOR HADOOP DATA 
LAKE 
- WITH JBOSS DATA VIRTUALIZATION
Data for entire organization in Hadoop Data Lake 
Problem: 
How 
does 
IT 
control 
access 
and 
give 
business 
users 
just 
the 
data 
they 
need? 
-­‐ 
Does 
every 
line 
of 
business 
have 
access 
to 
everyone’s 
data? 
-­‐ 
How 
do 
business 
users 
get 
access 
to 
the 
data 
they 
need 
in 
a 
simple 
(even 
self-­‐service) 
way? 
Hadoop 
Data 
Lake 
HR 
Employee 
Files 
25 RED 
HAT 
JBOSS 
MIDDLEWARE 
Marke<ng 
Clickstream 
Data 
Finance 
Expense 
Reports 
Server 
Logs 
Sales 
Transac<ons 
Customer 
Twiper 
Accounts 
Sen<ment 
Data
Secure, Self-Service Virtual Data Marts for Hadoop 
SoluFon: 
Use 
JBoss 
Data 
VirtualizaFon 
to 
create 
virtual 
data 
marts 
on 
top 
of 
a 
Hadoop 
cluster 
-­‐ Lines 
of 
Business 
get 
access 
to 
the 
data 
they 
need 
in 
a 
simple 
manner 
-­‐ IT 
maintains 
the 
process 
and 
control 
it 
needs 
-­‐ All 
data 
remains 
in 
the 
data 
lake, 
nothing 
is 
copied 
or 
moved 
Marke<ng 
Finance 
IT 
Hadoop 
Data 
Lake 
26 RED 
HAT 
JBOSS 
MIDDLEWARE 
Marke<ng 
Clickstream 
Data 
Customer 
Twiper 
Accounts 
Sen<ment 
Data 
Sales 
Server 
Logs 
HR 
Employee 
Sales 
Transac<ons 
Files 
Finance 
Expense 
Reports
Optional hierarchical data architectures with virtual 
data mart 
Can be combined with security features like user role 
access and row and column masking 
Dept 
Base 
Virtual 
Database 
(VDB) 
27 RED 
HAT 
JBOSS 
MIDDLEWARE 
Team 
1 
VDB 
Team2 
VDB 
View1 
View2
Virtual Data Marts for Operational Data 
Problem: 
All 
the 
legacy 
and 
archived 
data 
is 
in 
the 
Hadoop 
data 
lake. 
We 
want 
to 
access 
the 
most 
recent, 
up 
to 
the 
minute, 
operaFonal 
data 
oen 
and 
quickly. 
Hadoop 
Data 
Lake 
Historical 
Data 
HR 
Employee 
Files 
28 RED 
HAT 
JBOSS 
MIDDLEWARE 
Marke<ng 
Clickstream 
Data 
Finance 
Expense 
Reports 
Server 
Logs 
Sales 
Transac<ons 
Customer 
Accounts 
Twiper 
Sen<ment 
Data
Caching 
For 
Faster 
Performance 
– 
Materialized 
View 
Query 
1 
29 RED 
HAT 
JBOSS 
MIDDLEWARE 
Cached 
or 
Materialized 
View 
1 
View 
1 
Query 
2 
Virtual 
Database 
(VDB) 
• Same 
cached 
view 
for 
mul<ple 
queries 
• Refreshed 
automa<cally 
or 
manually 
• Cache 
repository 
can 
be 
any 
supported 
data 
source
Virtual operational data store 
SoluFon: 
Use 
JBoss 
Data 
VirtualizaFon 
to 
integrate 
up 
to 
the 
minute 
data 
from 
mulFple 
diverse 
data 
sources 
that 
can 
be 
quickly 
queried. 
-­‐ 
Use 
HDP 
for 
older 
data 
Materialized 
View 
30 RED 
Hadoop 
Data 
Lake 
HR 
Employee 
Files 
HAT 
JBOSS 
MIDDLEWARE 
-­‐ 
-­‐ 
Use 
JDV 
to 
materialize 
the 
data 
in 
HDP 
for 
-­‐ 
faster 
access 
and 
to 
combine 
with 
operaFonal 
VDB 
-­‐ 
Marke<ng 
Clickstream 
Data 
Finance 
Expense 
Reports 
Server 
Logs 
Sales 
Transac<ons 
Customer 
Accounts 
Twiper 
Sen<ment 
Data 
Opera<onal 
Historical 
Data 
VDB 
with 
up 
to 
the 
minute 
data 
Periodic 
Transfer 
from 
Data 
Sources
Demonstration 
Virtual Data Marts 
with 
Hadoop Data Lake 
31 RED 
HAT 
JBOSS 
MIDDLEWARE
Use Case 3 - Overview 
xxx ObjecFve: 
32 RED 
HAT 
JBOSS 
MIDDLEWARE 
–Purpose 
oriented 
data 
views 
for 
func<onal 
teams 
over 
a 
rich 
variety 
of 
semi-­‐structured 
and 
structured 
data 
Problem: 
–Data 
Lakes 
have 
large 
volumes 
of 
consolidated 
clickstream 
data, 
product 
and 
customer 
data 
that 
need 
to 
be 
constrained 
for 
mul<-­‐ 
departmental 
use. 
SoluFon: 
–Leverage 
HDP 
to 
mashup 
Clickstream 
analysis 
data 
with 
product 
and 
customer 
data 
on 
HDP 
to 
answer 
-­‐ 
Leverage 
Jboss 
Data 
Virt 
to 
provide 
Virtual 
data 
marts 
for 
Marke<ng 
and 
Product 
teams
Use Case 3 - Architecture 
33 RED 
HAT 
JBOSS 
MIDDLEWARE 
APPLICATIONS 
Business 
AnalyFcs 
Custom 
ApplicaFons 
Packaged 
ApplicaFons 
DATA 
SYSTEM 
SOURCES 
Emerging 
Sources 
(Sensor, 
SenFment, 
Geo, 
Unstructured) 
ExisFng 
Sources 
(CRM, 
ERP, 
Clickstream, 
Logs) 
HDP 
2.1 
Governance 
& Integration 
Security 
Operations 
Data Access 
Data 
Management 
VIRTUAL 
DATA 
MART
Use Case 3 - Resources 
• GUIDE 
How to guide: https://github.com/DataVirtualizationByExample/ 
HortonworksUseCase3 
Tutorial: Available soon 
• VIDEOS: 
http://vimeo.com/user16928011/hwxuc3configuration 
http://vimeo.com/user16928011/hwxuc3run 
http://vimeo.com/user16928011/hwxuc3overview 
• SOURCE: 
https://github.com/DataVirtualizationByExample/HortonworksUseCase3 
34 RED 
HAT 
JBOSS 
MIDDLEWARE
Demonstration 
Combining Sentiment Data 
with Sales Data 
35 RED 
HAT 
JBOSS 
MIDDLEWARE
Use Case 1: Combine data from 
Hadoop with traditional data 
sources 
Problem: 
Data from new data sources like 
social media, clickstream and 
sensors needs to be combined 
with data from traditional sources 
to get the full value. 
36 RED 
HAT 
JBOSS 
MIDDLEWARE 
Solution: 
Leverage JBoss Data Virtualization 
to mashup new data in Hadoop 
with data in traditional data 
sources without moving or 
copying any data and access it 
through a variety of BI tools and 
SOA technologies. 
Data 
can 
be 
accessed 
by 
mulFple 
tools 
and 
methods 
already 
in-­‐house 
Consume 
Compose 
Connect 
JBoss 
Data 
Virtualiza<on 
Hiv 
e 
SOURCE 
1: 
Hive/Hadoop 
contains 
data 
from 
new 
data 
sources 
like 
social 
media, 
clickstream 
and 
sensor 
data 
SOURCE 
2: 
TradiFonal 
relaFonal 
databases 
in 
the 
enterprise
Use Case 1 - Architecture 
RDBMS 
EDW 
MPP 
37 RED 
HAT 
JBOSS 
MIDDLEWARE 
DATA 
SYSTEM 
TRADITIONAL 
REPOSITORIES 
APPLICATIONS 
Business 
AnalyFcs 
Custom 
ApplicaFons 
Packaged 
ApplicaFons 
VIRTUAL 
DATA 
MART
38 RED 
HAT 
JBOSS 
MIDDLEWARE 
Use Case 1 – Demo
Use Case 1 - Resources 
http://hortonworks.com/hadoop-tutorial/evolving-data-stratagic- 
asset-using-hdp-red-hat-jboss-data-virtualization/ 
39 RED 
HAT 
JBOSS 
MIDDLEWARE
Benefits 
of 
Data 
Virtualiza<on 
on 
Big 
Data 
● Enterprise 
democra<za<on 
of 
big 
data 
● Any 
repor<ng 
or 
analy<cal 
tool 
can 
be 
used 
40 RED 
HAT 
JBOSS 
MIDDLEWARE 
● Easy 
access 
to 
big 
data 
● Seamless 
integra<on 
of 
big 
data 
and 
small 
data 
● Sharing 
of 
integra<on 
specifica<ons 
● Collabora<ve 
development 
on 
big 
data 
● Fine-­‐grained 
security 
of 
big 
data 
● Speedy 
delivery 
of 
reports 
on 
big 
data
41 RED 
HAT 
JBOSS 
MIDDLEWARE 
QUESTIONS

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Red Hat - Presentation at Hortonworks Booth - Strata 2014

  • 1. Discover Red Hat and Hortonworks for the Modern Data Architecture Kimberly Palko Product Manager Red Hat 1 RED HAT JBOSS MIDDLEWARE
  • 2. 2 RED HAT JBOSS MIDDLEWARE Agenda ● Red Hat and JBoss Middleware Overview ● Combining data in Hadoop with traditional data sources ● Federating two geographically distributed Hadoop clusters ● Virtual data marts for Hadoop Lake
  • 3. RED HAT & JBOSS MIDDLEWARE OVERVIEW 3 RED HAT JBOSS MIDDLEWARE
  • 4. Engineering CollaboraFon Benefits Integra<on with JBoss Data Virtualiza<on Enable agile Big Data Hadoop integra<on with exis<ng enterprise assets and maximize universal data u<liza<on to enable self-­‐service analy<cs 4 RED HAT JBOSS MIDDLEWARE Integra<on with mul<ple Red Hat JBoss Middleware product family Enables millions of JBoss developers to quickly build applica<ons with Hadoop Integra<on with Red Hat Storage Enables Hadoop to use Red Hat Storage secure resilient storage pool for data applica<ons Integra<on with Red Hat Enterprise Linux OpenStack PlaOorm Simplifies automated deployment of Hadoop on OpenStack Integrated with Red Hat Enterprise Linux and OpenJDK Develop and deploy Apache Hadoop as an integrated component for mul<ple deployment scenarios
  • 5. Big Data Integra<on: Turn Data into Ac<onable Informa<on Speed of Itera<on leads to Success Semi / Unstructured Data 5 RED SOCIAL, LOGS HAT JBOSS MIDDLEWARE Hadoop & NoSQL Data Integra<on & Data Services JBoss Data Virtualiza<on In-­‐memory data management JBoss Data Grid BI Analy<cs (diagnos<c, descrip<ve, predic<ve, prescrip<ve) SOA Applica<ons Event Processing & Messaging JBoss BRMS & JBoss A-­‐MQ Structured Data DW, OLAP, OLTP Streaming Data EVENTS, IOT Red Hat Enterprise Linux Red Hat Storage Analyze Integrate Enrich Ingest
  • 6. Data Challenges Geang Bigger… HBase 6 RED HAT JBOSS MIDDLEWARE NoSQL Hive MapReduce HDFS Storm Spark
  • 7. Make Big Data Accessible for Everyone 7 RED HAT JBOSS MIDDLEWARE
  • 8. Data Supply and Integration Solution Data Virtualiza<on sits in front of mul<ple data sources and ! allows them to be treated a single source 8 RED HAT JBOSS MIDDLEWARE ! delivering the desired data ! in the required form ! at the right <me ! to any applica<on and/or user. THINK VIRTUAL MACHINE FOR DATA
  • 9. Easy Access to Big Data Hive 9 RED ● Repor<ng tool accesses the data virtualiza<on server via HAT JBOSS MIDDLEWARE rich SQL dialect ● The data virtualiza<on server translates rich SQL dialect to HiveQL ● Hive translates HiveQL to MapReduce ● MapReduce runs MR job on big data MapReduce HDFS Analytical Reporting Tool Data Virtualization Server Hadoop Big Data
  • 10. Different Users Different Views of Big Data Hive 10 RED ● Logical tables with different forms of aggrega<on ● Logical tables containing extra derived data ● Logical tables with filtered data ● All reports/users share the same specifica<ons HAT JBOSS MIDDLEWARE MapReduce HDFS
  • 11. USE CASE 1: COMBINING DATA FROM HADOOP WITH TRADITIONAL SOURCES -­‐ USING JBOSS DATA VIRTUALIZATION 11 RED HAT JBOSS MIDDLEWARE
  • 12. Integra<on of Big Data with “Small Data” 12 RED HAT JBOSS MIDDLEWARE • Integra<ng small data with big data is easy • Integra<on specifica<ons can be shared or be developed for individual reports MapReduce HDFS Database Hive Applica<on Server
  • 13. Hive 13 RED HAT JBOSS MIDDLEWARE Caching the Big Data • Caches to speed up interac<ve repor<ng • Caches to create a consistent view of big data • Different caches for different reports MapReduce HDFS
  • 14. 14 RED HAT JBOSS MIDDLEWARE USE CASE 2: GEOGRAPHICALLY DISTRIBUTED HADOOP CLUSTERS WITH DATA VIRTUALIZATION - SECURING DATA BY USER ROLE
  • 15. Role based access control 15 RED HAT JBOSS MIDDLEWARE Roles • Define roles based on organiza<on hierarchy Users • External authen<ca<on via Kerberos, LDAP, etc. VDB • Assign users and groups to a virtual data base
  • 16. 16 RED HAT JBOSS MIDDLEWARE Authentication Kerberos From client to the virtual data base Login Modules LDAP (MS Ac<ve Directory, OpenLDAP, etc.), any JAAS based security domain REST and Web Services WS-­‐UsernameToken HTTP Basic authen<ca<on SAML SAML authen<ca<on for web client applica<ons
  • 17. Audit Logging via Dashboard 17 RED HAT JBOSS MIDDLEWARE
  • 18. Row and Column Masking 18 RED -­‐ Row based masking Ex: keyed off geographic marker -­‐ Column masking to a constant, null, or a SQL statement Example: change all but the Last 4 digits in a credit card number to stars concat('****', substring(column, length(column)-­‐4)) HAT JBOSS MIDDLEWARE
  • 19. Summary of Security Capabili<es ● Authentication – Kerberos, LDAP, WS-UsernameToken, HTTP Basic, SAML 19 RED HAT JBOSS MIDDLEWARE ● Authorization – Virtual data views, Role based access control ● Administration – Centralized management of VDB privileges ● Audit – Centralized audit logging and dashboard ● Protection – Row and column masking – SSL encryption (ODBC and JDBC)
  • 20. Demonstration Geographically Distributed Hadoop Clusters with Data Virtualization - Securing Data by User Role 20 RED HAT JBOSS MIDDLEWARE
  • 21. Use Case 2: Federating across Geographically Distributed Hadoop Clusters Problem: Geographically distributed Hadoop clusters contains sensitive data like patient records or customer identification that cannot be accessed by other regions due to regulatory policy. IT needs access to all data, but users can only access the data in their region. 21 RED HAT JBOSS MIDDLEWARE Solution: Leverage JBoss Data Virtualization to provide Row Level Security and Masking of columns while federating across Hadoop clusters. Data can be accessed by mulFple tools and methods already in-­‐house Consume Compose Connect JBoss Data Virtualiza<on Hiv e Hadoop cluster in one geographic region Hiv e Hadoop cluster in a second geographic region
  • 22. Use Case 2 - Architecture APPLICATIONS 22 RED HAT JBOSS MIDDLEWARE DATA SYSTEM Business AnalyFcs Custom ApplicaFons Packaged ApplicaFons VIRTUAL DATA MART
  • 23. Use Case 2 - Resources 23 RED HAT JBOSS MIDDLEWARE • GUIDE How to guide: https://github.com/DataVirtualizationByExample/ HortonworksUseCase2 Tutorial: Available soon • VIDEOS: hpp://vimeo.com/user16928011/hortonworksusecase2short hpp://vimeo.com/user16928011/hortonworksusecase2short • SOURCE: hpps://github.com/DataVirtualiza<onByExample/HortonworksUseCase2
  • 24. 24 RED HAT JBOSS MIDDLEWARE USE CASE 3: VIRTUAL DATA MARTS FOR HADOOP DATA LAKE - WITH JBOSS DATA VIRTUALIZATION
  • 25. Data for entire organization in Hadoop Data Lake Problem: How does IT control access and give business users just the data they need? -­‐ Does every line of business have access to everyone’s data? -­‐ How do business users get access to the data they need in a simple (even self-­‐service) way? Hadoop Data Lake HR Employee Files 25 RED HAT JBOSS MIDDLEWARE Marke<ng Clickstream Data Finance Expense Reports Server Logs Sales Transac<ons Customer Twiper Accounts Sen<ment Data
  • 26. Secure, Self-Service Virtual Data Marts for Hadoop SoluFon: Use JBoss Data VirtualizaFon to create virtual data marts on top of a Hadoop cluster -­‐ Lines of Business get access to the data they need in a simple manner -­‐ IT maintains the process and control it needs -­‐ All data remains in the data lake, nothing is copied or moved Marke<ng Finance IT Hadoop Data Lake 26 RED HAT JBOSS MIDDLEWARE Marke<ng Clickstream Data Customer Twiper Accounts Sen<ment Data Sales Server Logs HR Employee Sales Transac<ons Files Finance Expense Reports
  • 27. Optional hierarchical data architectures with virtual data mart Can be combined with security features like user role access and row and column masking Dept Base Virtual Database (VDB) 27 RED HAT JBOSS MIDDLEWARE Team 1 VDB Team2 VDB View1 View2
  • 28. Virtual Data Marts for Operational Data Problem: All the legacy and archived data is in the Hadoop data lake. We want to access the most recent, up to the minute, operaFonal data oen and quickly. Hadoop Data Lake Historical Data HR Employee Files 28 RED HAT JBOSS MIDDLEWARE Marke<ng Clickstream Data Finance Expense Reports Server Logs Sales Transac<ons Customer Accounts Twiper Sen<ment Data
  • 29. Caching For Faster Performance – Materialized View Query 1 29 RED HAT JBOSS MIDDLEWARE Cached or Materialized View 1 View 1 Query 2 Virtual Database (VDB) • Same cached view for mul<ple queries • Refreshed automa<cally or manually • Cache repository can be any supported data source
  • 30. Virtual operational data store SoluFon: Use JBoss Data VirtualizaFon to integrate up to the minute data from mulFple diverse data sources that can be quickly queried. -­‐ Use HDP for older data Materialized View 30 RED Hadoop Data Lake HR Employee Files HAT JBOSS MIDDLEWARE -­‐ -­‐ Use JDV to materialize the data in HDP for -­‐ faster access and to combine with operaFonal VDB -­‐ Marke<ng Clickstream Data Finance Expense Reports Server Logs Sales Transac<ons Customer Accounts Twiper Sen<ment Data Opera<onal Historical Data VDB with up to the minute data Periodic Transfer from Data Sources
  • 31. Demonstration Virtual Data Marts with Hadoop Data Lake 31 RED HAT JBOSS MIDDLEWARE
  • 32. Use Case 3 - Overview xxx ObjecFve: 32 RED HAT JBOSS MIDDLEWARE –Purpose oriented data views for func<onal teams over a rich variety of semi-­‐structured and structured data Problem: –Data Lakes have large volumes of consolidated clickstream data, product and customer data that need to be constrained for mul<-­‐ departmental use. SoluFon: –Leverage HDP to mashup Clickstream analysis data with product and customer data on HDP to answer -­‐ Leverage Jboss Data Virt to provide Virtual data marts for Marke<ng and Product teams
  • 33. Use Case 3 - Architecture 33 RED HAT JBOSS MIDDLEWARE APPLICATIONS Business AnalyFcs Custom ApplicaFons Packaged ApplicaFons DATA SYSTEM SOURCES Emerging Sources (Sensor, SenFment, Geo, Unstructured) ExisFng Sources (CRM, ERP, Clickstream, Logs) HDP 2.1 Governance & Integration Security Operations Data Access Data Management VIRTUAL DATA MART
  • 34. Use Case 3 - Resources • GUIDE How to guide: https://github.com/DataVirtualizationByExample/ HortonworksUseCase3 Tutorial: Available soon • VIDEOS: http://vimeo.com/user16928011/hwxuc3configuration http://vimeo.com/user16928011/hwxuc3run http://vimeo.com/user16928011/hwxuc3overview • SOURCE: https://github.com/DataVirtualizationByExample/HortonworksUseCase3 34 RED HAT JBOSS MIDDLEWARE
  • 35. Demonstration Combining Sentiment Data with Sales Data 35 RED HAT JBOSS MIDDLEWARE
  • 36. Use Case 1: Combine data from Hadoop with traditional data sources Problem: Data from new data sources like social media, clickstream and sensors needs to be combined with data from traditional sources to get the full value. 36 RED HAT JBOSS MIDDLEWARE Solution: Leverage JBoss Data Virtualization to mashup new data in Hadoop with data in traditional data sources without moving or copying any data and access it through a variety of BI tools and SOA technologies. Data can be accessed by mulFple tools and methods already in-­‐house Consume Compose Connect JBoss Data Virtualiza<on Hiv e SOURCE 1: Hive/Hadoop contains data from new data sources like social media, clickstream and sensor data SOURCE 2: TradiFonal relaFonal databases in the enterprise
  • 37. Use Case 1 - Architecture RDBMS EDW MPP 37 RED HAT JBOSS MIDDLEWARE DATA SYSTEM TRADITIONAL REPOSITORIES APPLICATIONS Business AnalyFcs Custom ApplicaFons Packaged ApplicaFons VIRTUAL DATA MART
  • 38. 38 RED HAT JBOSS MIDDLEWARE Use Case 1 – Demo
  • 39. Use Case 1 - Resources http://hortonworks.com/hadoop-tutorial/evolving-data-stratagic- asset-using-hdp-red-hat-jboss-data-virtualization/ 39 RED HAT JBOSS MIDDLEWARE
  • 40. Benefits of Data Virtualiza<on on Big Data ● Enterprise democra<za<on of big data ● Any repor<ng or analy<cal tool can be used 40 RED HAT JBOSS MIDDLEWARE ● Easy access to big data ● Seamless integra<on of big data and small data ● Sharing of integra<on specifica<ons ● Collabora<ve development on big data ● Fine-­‐grained security of big data ● Speedy delivery of reports on big data
  • 41. 41 RED HAT JBOSS MIDDLEWARE QUESTIONS