This document discusses the challenges of data modeling in the era of big data and the need for data modeling tools to evolve to represent both traditional relational databases and non-relational data stores used in big data systems. It provides an overview of how the data landscape has changed with the rise of big data and NoSQL databases. It also describes a proof of concept project where CA used its data modeling tool to reverse engineer and represent both relational and non-relational data from its products stored in a Hadoop cluster. The document argues that a unified view of all enterprise data spread across different data systems is needed and possible.

1. A Unified Data
Modeler in the World
of Big Data
William Luk, CA Technologies Inc
2012
Sr Director, Software Engineering – Data Modeling Collaboratio
Session Code: HT01 n By
Design

2. Speaker Bio
Senior Director of software
development in the Data
Management BU, head of
ERwin engineering and level
2 support
 Experience in
databases, data
security, and data
management;
 BS & MS in CS;

3. A Unified Data Modeler in the World of Big Data
Session Agenda
— Where are we & how do we get here?
— Overview of the Big Data world
— Challenges to enterprises and data architect
— Extending data modeling to include Big Data
—Q&A
3

4. Data Modeling Past 30 Years
— Entity-Relationship (ER) modeling has served us well
since mid-70’s
— Data architects / modelers have used ER tools to
ensure data consistencies and integrities for very
large enterprises
— Ability to integrate new databases from mergers and
acquisitions;
— A map of where all your data;
— Ability to handle large & complex data model;
— Then, the Internet & social networks
4

5. Internet & Social Networks
— Early Internet used the classical LAMP stack – Linux,
Apache Web Server, MySQL Database, and
Perl/PHP/Python
— Basic web servers & DB’s served us well for basic
web portal
— Internet growth + social networks changed the scale
of database / data store
— Traditional relational databases have difficulties
handling the scale & required (sub second) response
time for web
— Emergences of NoSQL data store
5

6. Arrival of Big Data
— Wealth of valuable data to collect:
− Users entered information
− History / logs of users interaction
— Not always fit nicely into structured data stores
(relational or NoSQL)
— Need to harvest / analyze the data to compete
— Challenges of
capturing, storing, searching, anlayzing, and
visualizing very large and complex data sets
— Large, distributed, analytical platforms (Hadoop)
emerged
6

7. Enterprise Big Data / Hadoop Workflow
Customer Data Source
HQL (Hive SQL),
JSON, XML, …etc Unstructured Data / Files
HDFS
Structured Data Semi-structured Data Unstructured Data
JSON
Hive HBas XML JSON
e
MapReduce /
Analytics
Hadoop Framework (Pig, Cloudera,
(Clusters) Datameer, …etc)
A Unified Data Modeler in the World of Big Data

8. Problem of Non-Relational Data Stores
— NoSQL and unstructured data store performance has
a price:
− Denormalized data
− Data consistencies & integrities – only guarantee “eventual”
consistency
— Some data (such as user comments) can tolerate
these drawbacks
— Some data (such as financial, transactional) cannot
— Enterprises conclusions:
− NoSQL & Big Data are good for business intelligences data
− Financial & transactional data still require relational databases
− Compliance requirements / regulations
8

9. The New World of Data Modeling with Relational
and Big Data
— The new enterprise data landscape:
− Different relational databases
− Distributed hadoop cluster with structured, semi-structured,
and unstructured data which is constantly changing
— Challenges to the data architects / modelers:
− Identify potential relationships between different data stores
− Automated way to track and update the unified view
— Data Modeling tools, such as ERwin, need to evolve
to present a single unified view of ALL enterprise
data
9

10. ERwin Tapping into Hadoop
Data Sources
JSON / XML Headers
HQL (Hive SQL),
JSON, XML,
Unstructured Data / Files
HDFS
Structured Data Semi-structured Data Unstructured Data
JSON
Hive HBas XML JSON
HQL e
MapReduce /
Analytics
Hadoop Framework (Pig, Cloudera,
(Clusters) Datameer, …etc)
A Unified Data Modeler in the World of Big Data

11. CA Internal Proof of Concept
Big Data of CA Enterprise Products
APM, Clarity,
Nimsoft, • CA Hadoop test framework
WatchMouse, …etc with 7 Dell 2950’s
Unified View of
CQL (Cassandra SQL), • Dump / store logs & data from
All Models HQL (Hive SQL),
mongoDB, various CA products into
JSON, XML HDFS
Reverse Engineer • Transform logs & data into
JSON / XML Headers
structured or semi-structured
data stores
CA Hadoop Test Semi-structured Data • Reverse engineer to build
Framework (HDFS / Cassandra FS) logical model of different CA
JSON products
XML JSON
• Identify potential relationships
Cassandra / between data stores
Reverse Engineer Hive / Hbase /
CQL / HQL /
Mongo Query
mongoDB
(JSON)
A Unified Data Modeler in the World of Big Data

12. What We Learn So Far
— Most non-relational data store will be a simple entity / box in
ERwin
− Attributes in each non-relational entity include key indices and columns
− Supercolumns or nested structures can be expanded in the same entity
or depict as hierarchy
— Metadata are important:
− Describes the kind of information / data
− Structure of the columns in a supercomlumn
— There are relationships between non-relational data stores and
relational databases
— So far, we only investigated reverse engineering of data stores
into logical model. Forward engineering of logical model into
physical non-relational data stores may be useful
— We are not there yet, but a unified data modeler of relational and
Big Data is definitely possible
12

13. The Future of Data Modeling
— Presented a (but not the only) direction that data
modeling can be evolved to model both relational
and non-relational data stores
— Data explosion will continue and accelerate at a
much faster rate
— Business must rely more and more on collected data
to gather business intelligence to compete
— Role of data architect and modeler will become more
important – to analyze Big Data, enterprises must
first understand what they have!
13

14. Thank You – Questions?
William Luk
(650)298-3111
William.luk@ca.com
http://www.linkedin.com/pub/william-luk/1/818/bb1

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