Dear Oracle Apps Jobs,
If you are working in Business Intelligence and Datawarehousing domain and thinking - "What is next?" , We have a good news for you. Now think beyond routine Development and Design tasks. Build your career as an Data Architect !!
Enroll for self paced Advanced Data and Dimensional Modeling Course.
Now get a special discounted price. This course is now available at only Rs 10,999 + Servcice Tax.
See the details at Course Link OR Please call us at +91-80-88861391 to know more about this course and offer.
Check the course content as below:
1) Introduction to Data Modeling
Learning Objectives
Introduction of Data Modeling and Data Models.
Topics
Data Modeling, Importance and Characteristics of a Good Data Model, Types of Data Models.
2) Normalization
Learning Objectives
Learn how important is normalization. Learn various forms of Normalization.
Topics
First Normal Form, Second Normal Form, Third Normal Form, Boyce-Code Normal Form (BCNF), Fourth Normal Form, Fifth Normal Form.
3) The Entity Relationship (E-R)
Learning Objectives
Learn about Entity Relationship framework.
Topics
Symbols for E-R, Entity, Dependent and Independent Entity Classes, Examples of relationships, One-to-One Relationships, Self-Referencing Relationships, Relationships Involving Three or More Entity Classes, Transferability, Relationships with Attributes, Roles, The Weak Entity Concept.
4) Subtypes and Supertypes
Learning Objectives
Learn why the concept of Subtypes/Supertypes is important for Data Modeling.
Topics
Non-overlapping and Exhaustive, Overlapping Subtypes and Roles, Benefits of Using Subtypes and Supertypes.
5) Conceptual Data Model
Learning Objectives
Learn about Conceptual Data Model. This is the first step in building a Data Model.
Topics
Using Pattern/ Generic Model, Bottom-Up Modeling, Top-Down Modeling, Which is better- Top Down or Bottom up?, Data Modeling based on Subject Area, Hierarchies, Networks, and Chains.
6) Logical Database Design
Learning Objectives
Learn about Logical Data Model. This is the second step in building a Data Model.
Topics
Introduction, Overview of the Transformations, Table Specification, The Standard Transformation, Exclusion of Entity Classes from the Database, Classification of Entity Classes, Many-to-Many Relationship Implementation, Relationships Involving More than Two Entity Classes, Supertype/Subtype Implementation, Basic Column Definition , Attribute Implementation, Category Attribute Implementation, Derivable Attributes, Attributes of Relationships, Complex & Multi-valued Attributes,Column Datatypes,Column Nullability,Primary Key Specification,Foreign Key Specification,One-to-Many Relationship Implementation,One-to-One Relationship Implementation,Derivable Relationships,Optional Relationships,Table and Column Names.
7) Physical Database Design
Learning Objectives
Learn about Physical Data Model. This is the final step in building a Data Model.
Topics
Inputs required for Physical Data Model,PDM Design Options,Indexes, Index Properties,Types of Indexes,Data Storage,Table Space Usage,Free Space,Locking, Views,Views of Supertypes and Subtypes ,Inclusion of Derived Attributes in Views, Views of Split and Merged Tables.
8) ERwin Tool
Learning Objectives
Learn ERwin. Learn basics and some of the advanced features of ERwin.
Topics
We will teach some of the basic and advanced features of ERwin. We will give a project assignment on ERwin, which is to create a Datamart data model on ERwin and execute it on a database.
9) Introducing Dimensional Modeling
Learning Objectives
Introduction of Dimensional Modeling.
Topics
Dimensional Modeling, Comparison of Dimensional Modeling with E-R Modeling or Normalized Modeling, Benefits of Dimensional Modeling.
10) Dimensional Modeling Building Blocks
Learning Objectives
Learn about all basic building blocks of Dimensional Modeling.
Topics
Fact Tables,Fact Table Keys,Fact Table Granularity,Dimension Tables,Dimension Features ,The Basic Structure of a Dimension,Dimension Table Keys ,Conformed Dimensions ,Grain ,Sparsity,Degenerate Dimensions,Role-Playing Dimensions ,Junk Dimensions ,Big Dimensions ,Small Dimensions ,Dimension co-relation,Date and Time Dimensions , Bus Architecture, Common Matrix definition Mistakes,Few tips while defining Matrix , Dimensional Design Process.
11) Slowly Changing Dimensions
Learning Objectives
Learn why Slowly Changing Dimensions is an important concept in Data warehousing and how to deal with various types of Slowly Changing Dimensions.
Topics
Slowly Changing Dimensions,Type 1: Overwrite the Dimension Attribute,Type 2: Add a New Dimension Row,Type 3, Add a New Dimension Attribute,Hybrid Slowly Changing Dimension Techniques,Conflicting Requirements, Frozen Attributes,Time-Stamped Dimensions,Mini-Dimensions for large dimension tables.
12) Snowflake, Outriggers, Hierarchy and Bridge Table
Learning Objectives
Learn about Snowflake, Outriggers, Hierarchy and Bridge Table.
Topics
Snowflake,Outrigger ,Eliminating Repeating Groups with Outriggers,Outriggers and Slow Change Processing, Hierarchies ,Fixed Hierarchies ,Variable Depth Hierarchies,Many-Valued Dimensions with Bridge Tables,Time-Varying Bridge Tables.
13) Late-Arriving Dimension Records and Correcting Bad Data
Learning Objectives
Learn how to handle when fact data arrives first and dimension data arrives later. This can create an integrity issue if not dealt with properly.
Topics
Dimension Data Value arrives after Fact Value is loaded,A Correction in Dimension Data Value arrives late.
14) More on Facts
Learning Objectives
Learn in detail about Fact tables and how to deal with complex types of Fact tables.
Topics
Three Fundamental Grains,Transaction Fact Tables, Periodic Snapshot Fact Tables,Accumulating Snapshot Fact Tables,Facts of Differing Granularity and Allocation ,Multiple Currencies and Units of Measure ,Factless Fact Tables ,Consolidated Fact Tables.
15) Detail on Transactions, Snapshots, and Accumulating Snapshots
Learning Objectives
Learn about Transactions, Snapshots, and Accumulating Snapshots fact tables.
Topics
Transaction Fact Tables,Describing events ,Properties of Transaction Fact Tables, Grain of Transaction Fact Tables,Transaction Fact Tables Are Sparse, Transaction Fact Tables Contain Additive Facts,Snapshot Fact Tables,Sampling Status with a Snapshot, Semi-Additivity, Snapshot Considerations, Pairing Transaction and Snapshot Designs,Additional Facts,Period-to-Date Measurements,Snapshots and Slow Changes,Accumulating Snapshot Fact Tables,Challenge: Studying Elapsed Time between Events,Tracking Process Steps in a Transaction Fact Table,Where the Transaction Model Falls Short ,Begin and End Dates Are Not the Answer,The Accumulating Snapshot,Life Cycle of a Row,Accumulating Snapshot Considerations , Pairing Transaction and Accumulating Snapshot Designs ,Focus on Key Status Milestones , Multi-source Process Information ,Nonlinear Processes, Slow Changes.
16) Details of Factless Fact Tables
Learning Objectives
Learn about Factless Fact table and why is it important for Datawarehousing.
Topics
Factless fact table for Events,Adding a Fact,F actless fact table for Conditions, Coverage, or Eligibility,Slowly Changing Dimensions and Conditions.
17) Designing the Dimensional Model
Learning Objectives
Learn how to create a Dimensional Model.
Topics
Prerequisite,The Team,The Requirements, Modeling Tools,Naming Conventions ,Provision for Source Data Research and Profiling,Obtain Facilities, Supplies and Time booking,Four-Step Modeling Process,Design the Dimensional Model,Build the High Level Dimensional Model,Conduct the Initial Design Session,Document the High Level Model Diagram,Identify the Attributes and Metrics,Develop the Detailed Dimensional Model,Identify the Data Sources,Understand Candidate Data Sources,Profile and Select the Data Sources,Establish Conformed Dimensions,Identify Base Facts and Derived Facts,Document the Detailed Table Designs, Update the Bus Matrix , Review and Validate the Model,Finalize the Design Documentation.
18) Some Important topics
Learning Objectives
In this module, you will learn about some important topics to deal with Dates, Indexing and Keys.
Topics
Start Date/End Data versus Effective Date/Expiry Date,Security Code ,Indexing and Partitioning ,Primary Index/ Primary Key,Secondary Indexes,Surrogate Keys and Natural Keys,Cubes,Cubes and the Data Warehouse.
19) A Fact Table for Each Process
Learning Objectives
Learn how is it important to have a separate fact table for each process and what complications can occur if this is not followed.
Topics
Example of Events occurring at different times,Example of Facts having different Grains, How to compare and analyze facts from multiple fact tables?,Drilling Across,Drill-Across Procedure.
20) Conformed Dimensions
Learning Objectives
Learn why Conformed Dimensions is a base to create the Data warehouse. Learn about various types of Conformed Dimensions.
Topics
Types of Dimensional Conformance,Shared Dimension Tables,Conformed Rollups,Conforming Degenerate Dimensions,Overlapping Dimensions.
21) More on Dimension Tables
Learning Objectives
Learn in-depth details of Dimension Tables.
Topics
The Browsability Test,Grouping Dimensions into Tables,Grouping Dimensions Based on Affinity ,Breaking Up Large Dimensions ,Splitting Dimension Tables Arbitrarily ,Drawbacks to Arbitrary Separation ,Alternatives to Split Dimensions ,Dimension Roles and Aliases,Problems created by NULL and their resolution ,Some other Special-Cases,Invalid Data ,Late-Arriving Data,Future Events,Behavioral ,Past Association with another Dimension ,Historic Fact,Categorizing Facts.
22) Hierarchies
Learning Objectives
Learn why Hierarchies are an important concept for Data warehousing. Learn various forms of Hierarchies.
Topics
Drilling ,Attribute Hierarchies and Drilling ,Drilling Within an Attribute Hierarchy,Other Ways to Drill,Multiple Hierarchies in a Dimension,Crossing between Dimensions,Instance Hierarchies ,Documenting Attribute Hierarchies ,Cube Design and Management.
23) Multi-Valued Dimensions and Bridges
Learning Objectives
Learn about Multi-Valued Dimensions and Bridges and why should they be managed properly to avoid any kind of double counting.
Topics
Standard One-to-Many Relationships,Simple Solution,Using a Bridge for Multi-Valued Dimensions , Bridges and Double-Counting,When Double-Counting Is Wanted,Adding an Allocation Factor,Supplementing the Bridge with a Primary Member , Impacts of Bridging, Slow Changes ,Resolving the Many-to-Many Relationship,Multi-Valued Attributes,Simplifying the Multi-Valued Attribute,Using an Attribute Bridge,Double-Counting,Primary Member and Hiding the Bridge ,The Impact of Changes,Resolving the Many-to-Many Relationship.
24) Recursive Hierarchies and Bridges
Learning Objectives
Learn about Recursive Hierarchies and Bridges. Recursive Hierarchies can be useful in creating generic Hierarchies.
Topics
The Reporting Challenge,Flattening a Recursive Hierarchy,Drawbacks of Flattening,The Hierarchy Bridge,Looking Down, Looking Up,Avoiding Double-Counting,Hiding the Bridge from Novice Users,Resolving the Many-to-Many Relationship,Looking Down Without a Many-to-Many Relationship, Looking Up Without a Many-to-Many Relationship,Changes and the Hierarchy Bridge,Type 1 Changes in the Dimension or Bridge, Type 1 Change to the Dimension,Type 1 Change to the Hierarchy,Type 2 Changes to the Dimension,Type 2 Changes to the Hierarchy , The Reason for the Ripple Effect,Multiple Hierarchies.
25) Type-Specific Stars
Learning Objectives
Learn how to make dimensional design more intelligent by using a Core Star and Custom Star concepts.
Topics
The Single-Star Approach, Drawbacks to the Single-Star Approach, Core and Custom Stars, Slowly Changing Dimensions, Core and Custom Fact Tables, The Same Facts for All Types ,Type-Specific Facts, Other Considerations, Overlapping Custom Dimensions, The Outrigger Alternative, Using Generic Attributes.
26) Derived Schemas
Learning Objectives
Learn about Derived Schemas. Derived Schemas pre-compute the results and store them separately to improve the performance.
Topics
Uses for Derived Schemas,Query Performance,Report Complexity, Data Subset,Use of Cubes,The Cost of Derived Schemas,Types of Derived Schemas,The Merged Fact Table ,Precomputed Drill-Across Results,Advantage of Merged Fact Table ,The Pivoted Fact Table,The Need to Pivot Data,The Pivoted Advantage,Drawbacks to Pivoting,The Sliced Fact Table,Creating Slices of a Star ,Uses for Sliced Fact Tables,Set Operation Fact Tables.
27) Aggregates
Learning Objectives
Aggregate is a very important concept of Data warehousing. It is used to improve the query performance.
Topics
Summarizing Base Data,Conformance,Using Aggregates,Writing (or Rewriting) Queries, Determining Which Star to Query,Loading Aggregates ,The Source of an Aggregate,Type 1 Changes,Type 2 Changes ,Aggregate Navigation, The Aggregate Navigator,Other Potential Benefits.
28) Project
Learning Objectives
In this module you will be working on a business problem where you need to design a data model with the help of ERwin Data Modeler tool. You will need to work on Logical, Conceptual and Physical data models. You will also learn how to build a Data model in ERwin and convert that to DDL code automatically.
Topics
Data Modeling project by using ERwin Data Modeler Community Edition.
29) Case Study: Retail
Learning Objectives
A Retail Data Model will be discussed during this case study.
30) Case Study: Telecom
Learning Objectives
A Telecom Data Model will be discussed during this case study.
Wishing you a great learning experience with BIWHIZ
Thanks & Regards,
Vibha from BIWhiz
Office Address: BIWHIZ, Second Floor, No. 1/1, old no 257/A, 46 Cross, Sangam Circle, 8th Block Jayanagar Bangalore-560070
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