BIG DATA MODELS
BIG DATA ARCHITECTURE
DATA MODELING
 Data Modeling
For change management
 Centerpiece for data quality program
 For data governance
 For Data querying

 Big data 
Non structural, NoSQL
 Schema-on-read  data is dumped as it comes, but a
schema or model is imposed as it is read or extracted
out

NOSQL DATA MODELING
 NoSQL data modeling often starts from the
application-specific queries
 Relational modeling  driven by the structure of
available data. ”What answers do I have?”
 NoSQL data modeling  driven by applicationspecific access patterns, i.e. the types of queries to be
supported. ”What questions do I have?”
 NoSQL data modeling  Requires a deeper
understanding of data structures and algorithms.
 NoSQL  Data duplication and denormalization are
first-class citizens.
 Relational databases  Not very suitable for
hierarchical or graph-like data modeling and
processing.
 For NoSQL, the data modeling approach varies
slightly depending on the type of NoSQL
database.
 There are 4 types of NoSQL databases:
Key:value (Oracle, Redis, Riak, numerous others)
 Key:Columnar(BigTable) (HBase, Cassandra, . .)
 Document Based (CouchDB, and MongoDB)
 Graph databases (neo4j, Giraph).

6 TIPS FOR MODELING BIG DATA
1. DO NOT IMPOSE TRADITIONAL
MODELING TECHNIQUES ON BIG DATA
 Big data growth is unpredictable / exponential,
since it comes from multiple sources.
 Focus should be on developing open and elastic
data interfaces, since its not known when a new
data source or form of data could emerge.
2. DESIGN A SYSTEM NOT A SCHEMA
 Big data favours for NoSQL types , which
requires no schema, so should be built on systems
 Thus a big data model should contain the
following system components:
Business information requirements
 Corporate governance and security
 The physical storage for data
 Integration and interfaces for all types of data
 Ability to handle a variety of different data types.

3. LOOK FOR BIG DATA MODELING TOOLS
 Commercial data modeling tools that support big
data management software like Hadoop, Tableau
 Focus should be on the ability to build data
models for big data
4. FOCUS ON DATA THAT IS CORE TO YOUR
BUSINESS
 Data grows exponentially day by day in an
enterprise.
 Extraneous data should be identified and ignored
off.
 Focus should be in identifying the big data that is
essential to the organization and then model that
data
5. DELIVER QUALITY DATA
 Each piece of data must be properly Qualified,
and understood.
 Data must be placed or mapped properly into the
data models that support the business.
6. LOOK FOR KEY INROADS INTO THE DATA
 Identify common keys or entry points into your
data.

Eg. One of the most commonly used vectors into big
data is geographical location.
 Helps in designing data models that support key
information access paths for your organization
DATA TRACEABILITY
 Knowledge of from where the data comes from for
each service at the higher level
where that data comes from,
 the data's path through the enterprise should be
thoroughly documented.

 The data flows in through various means, it is
consumed, transformed, and presented in the
form of an API.
 API consist of services, and each of these services
MUST either own the data they provide, or call
an appropriate service to get the data.