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WA3472

Enterprise Data Fundamentals Training

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.

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Course Details

Duration

2 days

Prerequisites

Basic knowledge of SQL and Python

Target Audience

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

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.
Course Outline
  • 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