BIG DATA COURSE 1 DATA QUALITY STRATEGIES - CUSTOMIZED TRAINING OUTLINE. Prepared by:

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Cerulium Corporation has provided quality education and consulting expertise for over six years. We offer customized solutions to maximize your warehouse.

Prepared by:

Steve Wilmes, CEO Cerulium Corporation Phone: 803.719.6782

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PURPOSE

This class, together with the Data Profiling Techniques class, provides a good beginning working knowledge of Data Quality. It equips students with a thorough understanding of all the issues surrounding data quality, and what can be done to improve it. The Data Quality Dimensions Methodology is presented as a

repeatable way to uncover and classify common data quality problems, and the classification scheme leads to the root causes and how they can be corrected. Metrics are presented which enable IT to partner and deliver to the business progress reports on data quality. Lastly, techniques on how and where to cleanse data are presented.

COURSE CURRICULUM:

This one-day class, together with the Data Profiling Techniques class, provides a good beginning working knowledge of Data Quality. The Data Architecture Boot Camp for Big Data (see below) is designed to address the critical need within the data warehousing industry for seasoned Data Architects.

BIG DATA – DATA QUALITY COURSE OUTLINE

Intended Audience: Business Analysts, Data Analysts, Data Administrators, Data Modelers, Data/System Architects, Database Administrators (DBA’s), Data Quality Managers, Application Development Managers, Data Warehouse

Managers, Project Managers

Course Agenda:

 What is Data Quality?

 The Role of Business Process in DQ  The DQ Dimensions

 How to Measure DQ & Metrics  How to Use Metrics

 How & Where to Cleanse Data  Data Cleansing Techniques

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PURPOSE

Enterprise BI™ is committed to providing participants with two levels of a BI perspective. The first level is focused on the data and technical architecture requirements needed to implement a best-of-class infrastructure for BI. The second level concentrates on exposing participants to leading BI tools, their use, and their application.

The course begins with a best-of-class, detailed examination of data and technical architectures specific to BI and for applications such as Query and Reporting, OLAP, Data Mining, Spatial Analysis, Real-time Analytics, Metadata, and Portal applications. Mobility will be introduced as a quick easy effect mechanism for agile methodology

Participants are then led through discussions on the proper application of leading BI tools, with regard to the architectures defined. Through lecture and live

product demonstrations, features and functionality are compared between products such as Hyperion Essbase, Microsoft Analysis Services, Cognos, and others. We will look at Hive, which runs on Hadoop, as a BI tool that can easily be implemented in the Enterprise for low cost solution.

COURSE CURRICULUM:

This three-day Enterprise BI™ course is designed to provide participants with a non-biased view of leading BI tools and how those tools fit into an overall BI architecture.

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ENTERPRISE BI STRATEGIES USING HADOOP COURSE OUTLINE

I. The BI Organization

o The Basis of Information o Components of a BI Strategy o Enterprise Deployment o Data Structure Dependency o A Conceptual Architecture o Applying the Right Tools o BI is an Iterative Process

o These Are Not Business Requirements II. Query & Reporting

o Definitions and Terms o Architecture

o Analysis, Design & Build Checklists o Tool Functionality

o Project Considerations III. Building an OLAP Solution

o OLAP & The BI Organization o OLAP Terms

o Dimensional Spectrum

o Business Question Components o Dimensional Diagrams

o A Tuple Cell o Hierarchies

o The Inflation Factor

o The Challenge With Sparsity IV. Meta Data

o Meta Data Terms o Benefits of Meta Data o Meta Data Impact

o The Meta Data Challenge o Common Model

o Point-to-point Bridges o Leading Approach o An Integrated View

o The Current State of Meta Data V. The BI Portal

o Portal Terms

o Decision Portal Definitions o Decision Portal Characteristics o Cross-Discipline Knowledge o Decision Based Design o Decision Factor Analysis o A Portal Implementation Method o The Architecture

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VI. Data Mining

o Data Mining & The BI Organization o Data Mining Terminology

o Data Mining Opportunities

o Mining Drives Analytic Applications o Data Mining Scenarios

o Application of Mining Techniques o Effort Distribution

VII. Spatial Data & Analysis

o Spatial Analysis & The BI Organization o Spatial Terminology

o What is GIS?

o Spatial Relationships o Displaying Spatial Data

o What Spatial Data Means to You VIII. Real-time Warehousing

o Terms o Zero-latency

o Real-time Data Warehousing o Business Activity Monitoring o Network & Systems Management IX. BI Trends

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Big Data Customized Education Outline

04/18/13

PURPOSE

This class is designed to expand on the skills of people that already have business analysis, systems analysis, and database design skills, and couple them with group facilitation skills, process modeling and data warehouse design skills. Upon completion of this course, the student will be able to facilitate the gathering of business information needs requirements and turn them into reliable and forward-thinking data warehouse designs complete with data models and specifications for ETL and Business Intelligence development.

COURSE CURRICULUM:

This five-day class is a hands-on, intense course that includes lecture, hands on labs, and nightly homework. The Big Data Boot Camp is designed to address the critical need within the data warehousing industry for seasoned Data Architects.

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04/18/13

BIG DATA BOOT CAMP COURSE OUTLINE

Day One

 Data Warehouse Basics

 Design Characteristics for Hadoop and MPP

 Architectural Considerations – Why go this way at all  Critical Success Factors

 Requirements Definition  Scope

 Process Modeling – The business way  Data Modeling

 Information Needs Modeling  Facilitation Management

 Purpose  Techniques

 Understanding Groups  Facilitator’s Tool Kit  LAB 1

Day Two

 Business Process Modeling  Context Diagramming

 Process and Flow Characteristics  Process Relationships

 Methodology  Validation Steps  LAB 2

 Enterprise Data Modeling  Definition and Use  Architecture  Mechanics  LAB 3

 Business Information Needs  Purpose and Use

 Focus on Business Objectives  LAB 4

Day Three

 Data Modeling Overview

 Logical vs Physical Models

 Logical Model Purpose and Components  Physical Model Purpose and Components  LAB 5

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 Data Modeling Entity-Attribute-Relationship Characterization  Understanding Entities and Attributes

 Representing Relationships and Hierarchies  Unique Identifiers - Best Practices

 Naming Standards  LAB 6

 The Data Modeling Process  Techniques

 Understanding the Normal Forms  Data Profiling

 LAB 7 Day Four

 Data Architecture Data Acquisition

 Data Warehouse Architectural Layers  Source Layer Considerations

 Audit Layer Considerations  LAB 8

 Data Architecture ETL Considerations  Stage Layer Design

 Audit/Stage ETL Design  LAB 9

 Data Architecture Base-History-Dimensional Design  Base Layer Design Standards

 History Layer Design Standards  Dimensional Design Standards  LAB 10

Day Five

 Modeling for BI Tool Capabilities  Business Tool Considerations  Leveraging RDBMS Capabilities  Leveraging BI Tool Capabilities  LAB 11

 Conclusions

 Data Warehouse Layers  Presentation Components  Audit Components

 Business Objective Focus  FINAL EXAM