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Enterprise Data Fundamentals

This Enterprise Data training course teaches attendees core concepts like data governance, quality, security, and storage to transform data into actionable insights. Through hands-on exercises,...

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$1,525 USD
Course Code WA3472
Duration 2 days
Available Formats Classroom

This Enterprise Data training course teaches attendees core concepts like data governance, quality, security, and storage to transform data into actionable insights. Through hands-on exercises, attendees learn to tackle common data management challenges, optimize operations, improve decision-making, and drive business growth.

Skills Gained

  • Understand the importance of effective data management in achieving organizational goals and objectives.
  • Learn about key concepts and principles of data management, including data governance, data quality, storage options, and data security.
  • Gain insights into common data management challenges and best practices for addressing them.
  • Understand the impact of poor data management on business operations, decision-making, and overall performance.
  • Learn about the practical aspects of data modeling and data security.

Who Can Benefit

  • IT Architects
  • Data Practitioners
  • Software Engineers
  • Business Analysts


Basic knowledge of SQL and Python

Course Details


Data Management Introduction

  • States of Digital Data
  • What is Data Management
  • The Core Components
  • Objectives
  • Timeliness
  • Data Management and Data Governance Relationship
  • Metadata
  • Information About Processes
  • Data Management Systems
  • Data Warehouses, Data Marts, and Data Lakes
  • High-Level Traditional Enterprise Data Flow
  • The Conceptual DW/BI Diagram
  • The Enterprise Data Problem Domain
  • ETL
  • Workflow (Pipeline) Orchestration Systems
  • Data Engineering
  • Data Management Best Practices

Data Governance

  • Data Governance
  • The DAMA-DMBOK Framework
  • Key Artifacts of Data Governance
  • Shared Environment Governance Controls
  • Best Practices
  • The Goldilocks Principle
  • Common Issues That Can be Prevented through Effective Governance
  • Ethics of Data Handling
  • Ethical AI

Data Architecture and Data Modeling

  • Data Architecture Defined
  • Data Modeling Defined
  • Data Architecture vs Data Modeling
  • A Data Model
  • Data Modeling and Design in Practice
  • Conceptual Data Models
  • The Entity-Relationship Model
  • Logical Data Models
  • Normalization
  • Normalization Forms
  • Physical Data Models
  • The Physical Data Model and DDL
  • The Star Schema
  • The Fact and Dimension Tables
  • Master Data Management

Data Storage Options

  • Storage Options
  • Which One Should I Choose?
  • Storage Location Options
  • Deciding on Database Type
  • Data Models
  • NoSQL Database Storage Types
  • A Key-Value Storage Type Example
  • Efficient Storage with Columnar Formats
  • Scalability
  • ACID Compliance
  • Cloud-Based Database Services
  • Creating a MySQL Database Instance Dialog
  • Key Concepts of Object Storage
  • Accessing Data in Object Stores
  • Content Delivery Networks
  • BigQuery
  • BigQuery Data Source Integrations
  • BigQuery Use Case: Migrating Data from Teradata
  • The Repository Type
  • Access Timeliness

Data Security

  • Security Domains
  • The CIAs of Security
  • Common Areas, Concerns, and Considerations
  • NIST Risk Management Framework
  • Vulnerability and Exploits
  • Ways to Eliminate (Mitigate) Vulnerabilities
  • The Inputs, Activities, and Deliverables Flow
  • Inputs
  • Activities
  • Deliverables
  • Distributed Identity Management
  • Access Control: Authentication & Authorization
  • Authorization and Data Access Constraints
  • Working Environments
  • Access Control: Auditing
  • Cloud Shared Responsibility Model (SRM)
  • The AWS SRM: The AWS Side of the Deal
  • The AWS SRM: Your Side of the Deal
  • Cloud Compliance Programs
  • DevOps Security Concerns
  • Agile Programming Concerns
  • Protecting Sensitive Data at Rest
  • Hashing
  • Secure Hashing Algorithm Family
  • Symmetric and Asymmetric (Public) Key Encryption
  • Security Best Practices
  • Be Aware of Spear-phishing Attacks

Data Quality Introduction

  • Data Quality Defined
  • An Opinion on Data Quality
  • The Great, Fast, and Cheap Quality Diagram
  • Data Quality Dimensions/Properties
  • Interpreting Data Quality Properties
  • Data Flow Potential Points of Failure
  • Data Quality Assurance
  • Common Factors Contributing to Poor Data Quality
  • Data Quality is a Shared Concern
  • Data Governance
  • Common Steps to Overcome Data Quality Issues
  • Data Observability
  • Application Performance Monitoring (APM) and Observability Magic Quadrant
  • Data Quality and Data Observability Relationship
  • A Glossary of Business Terms
  • Data Dictionaries
  • SLAs
  • SLAs and Non-Functional Requirements
  • SLAs Types
  • Data Integration and Data Integrity
  • IT Systems' Woes
  • Unified Data Platform
  • The Methods and Techniques to Ensure Data Quality
  • Maintenance
  • Automation
  • Data Formats
  • Interoperable Data
  • Data Validation
  • DDL-based Data Validation
  • The Schema Production and Consumption Diagram
  • Regular Expressions
  • Industry-Standard Data Models

Lab Exercises

  • Lab 1. Learning the Colab Jupyter Notebook Environment
  • Lab 2. Metadata
  • Lab 3. The Star Schema Project
  • Lab 4. Understanding the Normalization and MDM Connection
  • Lab 5. Evidence-Based SLA Metrics