Online Job Support for OLTP Data Modeling & Dimensional Data Modeling

CPT/OPT EAD/L2 EAD/H4 EAD holders:

If a new opportunity is provided and you are facing difficult challenges in understanding the business requirements to derive data models.

Data Modelers, Data Analyst,  ETL Developers and BI Developers:

Interview:

  • If you face tough scenario based  interview questions and not able to answer.

Less Productivity: 

  • If the duties and responsibilities are changed by the client for no reasons and if you are one among those who find it difficult to cope up with puzzling scenarios.

In Between Projects: 

  • If your roles and responsibilities must be updated in near future
  • If you are finding difficulties in data modeling (designing RDBMS database or Data Warehouse or Data Mart),  you want to take your knowledge and understanding of the Database design to the next level.

If you are the one who got struck in the above-mentioned scenarios or if you are finding difficulties in data modeling (designing RDBMS database or Data Warehouse or Data Mart), please approach Training@LearnDataModeling.com or 91-90801 57239.

What we can offer through ONLINE:

  • Our consultants have more than 15 plus years of experience in Data Modeling in normalized databases, Data Warehouses and Data Marts.
  • Has hands on with OLTP / Dimensional Data Modeling, OLAP Cubes, Informatica, Oracle Warehouse Builder, Cognos, Brio and SQL/PLSQL.
  • Provide solutions on data modeling.
  • Provide solutions on data analysis, business analysis, user expectations related to data modeling.
  • Share knowledge to implement complicated Data Modeling scenarios.
  • Will meet your deadlines on each individual task or group of tasks.
  • Can sign agreement on a daily basis/weekly basis/monthly basis.

 

Online Dimensional Data Modeling Training

Online Dimensional Data Modeling Training | Data Warehouse Training | Data Mart Training

Course Description:

The dimensional data modeling training explains how to design Data Ware House and Data Marts from OLTP data models.

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Course Information:
  • Mode of Training: Online Through GotoMeeting.
  • Start Date: Starts on 23rd June 2018 (Weekend Classes – 9.30 A.M EST to 11.30 A.M EST)
  • Course Fee: $125 (One hundred and seventy five US Dollars) per person
  • Mode of Payment: To USA Savings Account or India Savings Account or through www.Xoom.com
  • Total no. of theoretical/Practical classes: At least 10 Hours
  • Instructor: Neelesh (US Employee) & Antony (Owner of LearnDataModeling.com)
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Tools:
  • Erwin
  • MS Word, MS Excel
  • My SQL
  • Windows Operating System
Training Certificates:
  • Will be provided.

Course Syllabus

Part 1 – Dimensional Data Modeling (OLAP):
  • Need for Strategic Information
  • Examples of Business Objectives
  • Characteristics of Information
  • Operational Systems – OLTP
  • Examples of Operational Systems
  • Decision Support Systems – OLAP
  • Operational VS Decision Support
Part 2 – Introduction to Data Warehouse:
  • Data Warehouse Definition
  • Data Marts
  • Data Warehouse and Data Mart
  • Pioneer of Data Warehousing (Inmon)
  • Bill Inmon’s Approach (HUB and Spoke Architecture)
  • Pros and Cons of Inmon’s Approach
  • Pioneer of Data WareHousing (Ralph Kimball)
  • Ralph Kimball’s Approach (Bus Architecture)
  • Pros and Cons of Kimball’s Approach
  • What is ETL?
  • Things to learn for mapping/Data mapping
Part 3 – Business Intelligence:
  • OLAP
  • Dimensional Modeling
Part 4 – Different types of Fact Tables:
  • Transactional Facts
  • Snapshot or inventory
  • Factless Facts
  • Semi Additive and Non Additive Facts
Part 5 – Dimension Tables:
  • Dimension Types
  • Degenerate Dimensions
  • Denormalized Flattened Dimensions
  • Snowflaked Dimensions
  • Role-Playing Dimensions
  • Junk Dimensions
  • Outrigger Dimensions
Part 6 – Designing Star Schema:
  • Putting Building Blocks Together
  • Dimensional Model
  • Dimensional (Star) Schema
  • Declare the grain of Business Process
  • 4 Steps Dimensional Model Design Process
    • Identify Business Process
    • Identify Grain
    • Identify Dimensions
    • Identify Facts
Part 7 – Slowly Changing Dimension Techniques:
  • Type 0: Retain Original
  • Type 1: Overwrite
  • Type 3: Add New Attribute
  • Type 4: Add Mini-Dimension
  • Type 5: Add Mini-Dimension and Type 1 Outrigger
  • Type 6: Add Type 1 Attributes to Type 2 Dimension
  • Type 7: Dual Type 1 and Type 2 Dimensions
Part 7 – Big Data Data Modeling:
  • Design data model of large volumes of Data (Large Volumes of Data) with RDBMS structure

 

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Online Data Modeling Training – Crash Course

Crash Course on Data Modeling Training using ERWIN Tool

Course Description:

This training course explains in detail about database, data warehouse, data modeling concepts, data modeling types and how these are used in OLTP environments and Data warehouse / Datamart Environments. To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9884675745.

Course Brochure:

Course Syllabus

 

Part 1 – Career Path of a Data Modeler

  • What is a Data Modeling?
  • Explanation of Data Modeler duties in brief
  • Certifications in Data Modeling
  • Career Path of a Data Modeler
  • Salary of a Data Modeler

Part 2 – Data Modeling Concepts

  • Who is a data modeler?
  • What are the other alternative titles for a Data Modeler?
  • What are the duties and responsibilities of a Data Modeler?
  • What is the difference between duty and responsibility?
  • What is a Data Model?
  • Who needs Data Modeling?
  • Different Data Modeling Tools
  • IDEF1X and IE Methodology

Part 3 – Data Modeling Types

  • Logical Data Model
  • Physical Data Model
  • Dimensional Data Model
  • Conceptual Data Model
  • Enterprise Data Model
  • Data Modeling Development Life Cycle

Part 4– Data Model Standards

  • Naming standards of objects
  • Abbreviating column names
  • Consistency in Column Names
  • Why it is important

Part 5 – Database Explanation from Data Modeling Perspective

  • Main object: Table, Column, Datatype
  • Constraints: NULL, NOT NULL, Primary Key, Unique, Check, Default Value
  • Other objects: Database, Schema, Tablespace, Segment, Extent, Privileges, Index, View, Synonym
  • DDL Statements: CREATE, ALTER, DROP
  • DML Statements: INSERT, UPDATE, DELETE

Part 6– How to create a logical Data Model

  • Entity, Attribute, Primary Key, Alternate Key, Inversion Key Entry, Rule, Relationship, Definition, Index, Unique Index

Part 7– Relationships

  • Identifying, Non-Identifying, Many to Many
  • Cardinality
  • One to One Relationship
  • One to many relationship
  • Many to many relationship
  • Whether Zero option is required or not
  • Resolving Many to Many Relationship
  • Self-Referential Integrity Relationship
  • Normalization process
  • Supertypes and Subtypes

Part 8 – How to create a Physical data model:

  • Table, Column
  • Primary Key Constraint, Unique Constraint Check Constraint, Foreign Key Constraint, Comment
  • Default Value
  • Unique Index, Non-Unique Index,
  • Difference between a logical data model and Physical Data Model

Part 9 – Physical Data Model, Database & Scripts:

  • What is Forward Engineering?
  • How to generate scripts from a data model and share it with DBA?
  • What is Reverse Engineering?
  • How to create a data model from a database?
  • How to create a data model from a script?
  • How to compare data models?
  • How to compare database and a data model?
  • What is subject area?
  • Why do we need so many subject areas?
  • How to implement Physical data model in a database?
  • How to generate SQL Code?
  • How to implement it in Database?

Part 10 – Concepts: Dimensional Data Modeling, Data Warehouse and Data Mart

  • What is a Lookup?
  • How to maintain data in Lookups?
  • What is a Data Warehouse?
  • What is a Data Mart?
  • What is a Dimension?
  • What is Snow Flake Modeling?
  • What is Star Schema Modeling?
  • What is Slowly Changing Dimensions?
  • What is Dimensional Data Modeling?
  • How to create a data model for Data Warehouse and Data Mart?

 

Online Data Modeling Training Syllabus

Online Data Modeling Training on

OLTP, Data Warehouse, Datamart, Dimensional and Snow Flake Data Modeling and Normalization. end to end process with ERWIN Tool

 

Course# 1 – Learn Erwin data modeling to create Data Models

Course Description:

This online course explains how to use Erwin Data Modeling tool to create logical data model, conceptual data model and physical data model. It also explains how to create different objects like entity, attribute, relationship, null, not null, primary key, foreign keys, naming conventions, one to one relationship, one to many relationship, many to many relationship, identical relationship, non-identical relationship, default, domain, subject area, reports generation etc.

Course Duration:

3 hours to 4 hours through SKYPE or Goto Meeting.

Data Modeling sample used:

Training Institute Data Model

Course Start Time:

Any time.

Course# 2 – Online Advanced Data Modeling Training

Course Description:

This online training course explains in detail about database, data warehouse, data modeling concepts, data modeling types and how these are used in OLTP environments and Data warehouse / Datamart Environments. If you are fresher or a beginner, we will teach the fundamental concepts on one-to-one basis and later you will be enrolled in Course# 2 i.e Online Advanced Data Modeling Training.

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Course Information:
Start Date:
  • Batch I starts on 5th July, 2018 (Daily Classes – 9.15 P.M EST to 10.15 P.M EST)
  • Batch II starts on 07th July 2018 (Weekend Classes – 9.30 A.M EST to 11.30 A.M EST)
  • Course Name: Advanced Data Modeling Training through SKYPE or GotoMeeting.
  • Course Fee: $175 (One hundred and seventy five US Dollars) per person
  • Mode of Payment: To USA Savings Account or India Savings Account or through www.Xoom.com
  • Total no. of theoretical/Practical classes: At least 14 Hours
  • Mode of Teaching: Online through GoToMeeting
  • Instructor: Neelesh (US Employee) & Antony (Owner of LearnDataModeling.com)
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Tools:
  • Erwin
  • MS Word, MS Excel
  • My SQL
  • Windows Operating System
Training Certificates:
  • Will be provided.

Course Syllabus

Part 1 – Career Path of a Data Modeler

  • What is a Data Modeling?
  • Explanation of Data Modeler duties in brief
  • Certifications in Data Modeling
  • Career Path of a Data Modeler
  • Salary of a Data Modeler

Part 2 – Data Modeling Concepts

  • Who is a data modeler?
  • What are the other alternative titles for a Data Modeler?
  • What are the duties and responsibilities of a Data Modeler?
  • What is the difference between duty and responsibility?
  • What is a Data Model?
  • Who needs Data Modeling?
  • Different Data Modeling Tools
  • IDEF1X and IE Methodology

Part 3 – Data Modeling Types

  • Logical Data Model
  • Physical Data Model
  • Dimensional Data Model
  • Conceptual Data Model
  • Enterprise Data Model
  • Data Modeling Development Life Cycle

Part 4– Data Model Standards

  • Naming standards of objects
  • Abbreviating column names
  • Consistency in Column Names
  • Why it is important

Part 5 – Database Explanation from Data Modeling Perspective

  • Main object: Table, Column, Datatype
  • Constraints: NULL, NOT NULL, Primary Key, Unique, Check, Default Value
  • Other objects: Database, Schema, Tablespace, Segment, Extent, Privileges, Index, View, Synonym
  • DDL Statements: CREATE, ALTER, DROP
  • DML Statements: INSERT, UPDATE, DELETE

Part 6– How to create a logical Data Model

  • Entity, Attribute, Primary Key, Alternate Key, Inversion Key Entry, Rule, Relationship, Definition, Index, Unique Index

Part 7– Relationships

  • Identifying, Non-Identifying, Many to Many
  • Cardinality
  • One to One Relationship
  • One to many relationship
  • Many to many relationship
  • Whether Zero option is required or not
  • Resolving Many to Many Relationship
  • Self-Referential Integrity Relationship
  • Normalization process – 1NF, 2NF, 3NF
  • Supertypes and Subtypes

Part 8 – How to create a Physical data model:

  • Table, Column
  • Primary Key Constraint, Unique Constraint Check Constraint, Foreign Key Constraint, Comment
  • Default Value
  • Unique Index, Non-Unique Index,
  • Difference between a logical data model and Physical Data Model

Part 9 – Physical Data Model, Database & Scripts:

  • What is Forward Engineering?
  • How to generate scripts from a data model and share it with DBA?
  • What is Reverse Engineering?
  • How to create a data model from a database?
  • How to create a data model from a script?
  • How to compare data models?
  • How to compare database and a data model?
  • What is subject area?
  • Why do we need so many subject areas?
  • How to implement Physical data model in a database?
  • How to generate SQL Code?
  • How to implement it in Database?

Part 10: Concepts: Dimensional Data Modeling, Data Warehouse and Data Mart

  • What is a Lookup?
  • How to maintain data in Lookups?
  • What is a Data Warehouse?
  • What is a Data Mart?
  • What is a Dimension?
  • What is Snow Flake Modeling?
  • What is Star Schema Modeling?
  • What is Slowly Changing Dimensions?
  • What is Dimensional Data Modeling?
  • How to create a data model for Data Warehouse and Data Mart?
  • What is ETL?
  • Things to learn for mapping/Data mapping

Part 11:  Repository, Meta Data and Maintenance of the Data Model

  • What is a Repository?
  • What is Meta Data?
  • How to maintain the data model?
  • How to work in a multi-user environment

Part 12:  Data Modeling Training on Big Data:

While I write this, one should not think that this is “Big Data” Data Modeling. What I mean to write here is how to model the “big data”, which has very big data/huge volume/high velocity by using OLTP and OLAP Data Modeling.