A model is developed for a purpose. Understanding the strengths of each of the three Data Modeling types will prepare you with a more robust analyst toolkit. The program will describe modeling characteristics shared by each modeling type. Using the context of a reverse engineering exercise, delegates will be able to trace model components as they are used in a common data reengineering exercise that is also tied to a Data Governance exercise.
Learning objectives:
-Understanding the role played by models
-Differentiate appropriate use among conceptual, logical, and physical data models
- Understand the rigor of the round-trip data reengineering analyses
- Apply appropriate use of various Data Modeling types
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Quest Platform for Data Empowerment
Data Operations Data Protection Data Governance
Data Movement
Database Modeling
Data Systems
Performance Monitoring
Data DevOps and Preparation
Data Security and
Endpoint Management
Policy and Access Management
Audit and Compliance
Backup and Recovery
Data Catalog
Data Literacy
Data Profiling and Quality
Enterprise Architecture and
Business Process Modeling
Data Intelligence
Source Any Data From Anywhere to Empower Everyone
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Time is of the Essence
Applying big data analytics to
smaller data sets in near real
or real time
Critical to native cloud apps
that require low latency and
rely on high input / output
capability
Rapid ingestion of millions of
live data streams from
multiple endpoints
A streaming system that
delivers events as fast as
they come in
A data store that processes
each item as fast as it arrives
Real-time analytics and
complex decision-making
that helps effectively
process relentless
incoming data feeds.
Source: O’Reilly, Wired, TechTarget
Fast Data is…
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But Most Companies Still Don’t Know
What processes
should govern its use?
What data do I have
and where is it?
How is this data
relevant and
accessible to the
business?
What people and
systems are using
that data and for
what purposes?
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Enterprise Data Requirements
Harvest
Collect data schema
and business terms.
Analyze
Map data and
attributes.
Structure
Standardize
specific business
terms and
definitions.
Govern
Develop a
governance model
to manage
standards and set
best practices.
Visualize
Enable all
stakeholders to
see data in one
place in their own
context.
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erwin Data Literacy Suite
erwin Data Catalog Suite
Business User Portal
Business Glossary
Manager
Mapping Manager Lifecycle Manager
Reference Data
Manager
Data Quality
Data Intelligence Suite
Enterprise Modeling Suites
erwin Evolve
erwin Data Modeler
Data Automation
Standard Data Connectors Smart Data Connectors
erwin Enterprise Modeling & Data Intelligence Solutions
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Purpose &
Features
Why data modeling is better with erwin
erwin Data Modeler has been the most trusted name in data modeling for more than 30 years. The world’s top financial services, healthcare,
critical infrastructure and technology companies, including those on the Fortune 500, use the erwin modeling tool. In today’s data-driven
enterprise, its benefits have expanded to a wide range of architects, business analysts and data administrators to support their strategic initiatives.
These are some of erwin Data Modeler’s unique advantages:
Modern, customizable modeling environment
Automate complex and time-consuming tasks for more effective database design, standardization, deployment and maintenance across all your database platforms. Visualize
complex business and technical data structures, automatically generating data models in a single, intuitive interface.
Breadth of DBMS integrations & metadata bridges
Translate the technical format of the major cloud and on-premises database platforms into highly graphical models rich in metadata, thanks to built-in interfaces. erwin Data
Modeler also provides out-of-the-box bridges for metadata exchange and transformation from other modeling environments, data management platforms and metadata
exchange formats.
Model & database comparisons
The Complete Compare facility, with Quick Compare templates, automates bidirectional synchronization of models, scripts and databases; compares one item with another;
displays any differences and permits selective updates, generating ALTER scripts when necessary.
Roundtrip engineering
Forward- and reverse-engineering of database code and model exchange ensures efficiency, effectiveness and consistency in designing, standardizing, deploying and
documenting data structures for comprehensive enterprise database management.
Data catalog & business glossary integration
erwin Data Modeler is an essential source of, and one of the best ways to view, metadata. It’s a critical enabler of data governance and intelligence, so metadata from erwin data
models can be harvested automatically and then ingested into our data catalog and business glossary.
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Purpose &
Features
The application development process typically begins with a logical model that captures business requirements. Then, to transition from one design layer to
another, for example, from a logical model to a physical model, you derive a new model from an existing model. In this scenario, each model represents
a design layer in the application development process.
With erwin Data Modeler’s Design Layer Architecture (DLA) you can derive any model type from an existing model. Some of the more common derive scenarios
are:
• Create a business focused conceptual model which can be derived to a logical model.
• A logical model with more details based on the conceptual model.
• Derive a physical model to a specific a target database and version, and to enforce naming standards.
• Derive multiple physical models from a logical model. A generic physical model is a model in which you specify DBMS-independent design decisions.
• Derive a logical model from a logical model. For example, you can derive a new logical model based on a subject area (based on related objects) from the
source model.
The source model contains all model objects that you can include in a derived, or target model. When you derive a model, the source and target models are
automatically linked. Because the objects in the source and target model are linked, you can change the objects in either model, and at any time, synchronize the
two models. This allows you to maintain your design layer hierarchy.
If you choose to maintain historical information, the history for each entity, attribute, table, and column in a derived model is maintained. You can select a model
object from a derived model and review the model objects used to create the object.
erwin Data Modeler Feature – DLA and Model Derivation
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erwin Data Modeler – Conceptual Modeling
• Simple non-technical view of related
business objects
• Entity level
• Used as source for derived logical
models
• Provides focus and guidance to
modeling efforts
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Purpose &
Features
erwin Data Modeler – Logical Modeling
• Derived from conceptual model
• Required to achieve the transition from
conceptual to physical
• Developed to the attribute level and
understood at 3rd normal form
• Logical models are developed to be
refined to until it becomes a solution -
sometimes purchased (as in EDW)
always requires tailoring
• Used to guarantee the rigor of the data
structures by formally describing the
relationship between data items in a
strong fashion
• Not tied to any specific RDBMS
• Semantically linked to conceptual model
Semantic
linkage back to
conceptual
model
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erwin Data Modeler- Physical Modeling
Semantic
linkage back to
logical model
• Derived from logical model
• Become the blueprints for physical
construction of the solution
• DDL is generated from here
• Models are used for future
maintenance of the data structure
• Detailed information specific to target
DBMS
• Semantically linked to logical model