Faster data platforms. AI drafts. Experts decide.

Introduction to enterprise data architecture

Business requirements versus functional and non-functional requirements

Role of the Business Analyst, Data Architect, Data Modeler and Data Engineer

Relationship between business architecture, application architecture and data architecture

Challenges in translating business language into technical solutions

Requirement gathering techniques

Business requirement documents (BRDs)

Functional and non-functional requirements

Business process identification

Business scenarios, use cases and user stories

Requirement classification and prioritization

Requirement ambiguity, duplication, inconsistency and gaps

Business events and their lifecycle

Business rules and decision points

Operational versus analytical requirements

Business domains and subdomains

Data domain ownership

Master, reference, transactional and event data

Cross-domain data dependencies

Translating business activities into data requirements

Business objects versus data entities

Identifying data capture, update, retrieval and reporting requirements

Data attributes and business definitions

Data ownership and authoritative sources

Data retention and historical requirements

Operational, analytical and regulatory data needs

Source system identification

Source-to-business data mapping

Structured, semi-structured and unstructured data

Data profiling and source data assessment

Identifying missing, duplicate and conflicting data

Source system assessment and gap analysis

What is enterprise data architecture?

Data architecture principles and standards

Centralized, decentralized and federated architectures

Enterprise data architecture building blocks

Data architecture patterns

Data architecture artifacts and documentation

Landing and staging layers

Bronze, Silver and Gold architecture

Enterprise Data Warehouse architecture

Operational Data Store (ODS)

Data Lake and Lakehouse

Data marts and semantic layers

Batch versus streaming architecture

Data integration patterns

ETL versus ELT

API-based and event-driven integration

Data replication and CDC

Data transformation and enrichment

Error handling and recovery

Data latency and integration SLAs

Purpose of conceptual data models

Identifying business entities

Business entity relationships

Relationship types and cardinality

Enterprise conceptual data modeling

Logical data modeling methodology

Entities, attributes and relationships

Primary keys, alternate keys and candidate keys

Foreign keys and referential integrity

Identifying, non-identifying and recursive relationships

Associative entities and many-to-many relationships

Normalization: 1NF, 2NF and 3NF

Enterprise-wide entity reuse

Common business entities and shared data structures

Master data and reference data modeling

Transaction, event and status modeling

Effective dating and historical attributes

Address, contact and party modeling

Hierarchies and recursive relationships

Modeling complex business relationships

Model validation and quality checks

Logical model review and stakeholder approval

Introduction to dimensional modeling and Kimball methodology

Star schema and snowflake schema

Fact tables and dimension tables

Identifying business processes and fact table grain

Transaction, periodic snapshot and accumulating snapshot facts

Conformed dimensions

Role-playing, junk and degenerate dimensions

Slowly Changing Dimensions (SCD Types 0, 1, 2 and 3)

Introduction to Data Vault 2.0 architecture

Business Vault versus Raw Vault

Hub, Link and Satellite structures

Business keys and hash keys

Satellite historization and load tracking

Effectivity and record tracking satellites

Multi-active satellites

Point-in-Time (PIT) and Bridge tables

Enterprise data warehouse integration patterns

Data Vault to dimensional data mart transformation

Hybrid enterprise data architecture

Logical versus physical data models

Mapping logical entities to physical tables

Indexing and partitioning strategies

Denormalization and performance considerations

Database-specific implementation considerations

Forward and reverse engineering using ER/Studio

Data warehouse, data lake and Lakehouse architecture

Cloud data platform design considerations

Cloud storage and compute separation

Data ingestion and transformation services

Data platform scalability and elasticity

Platform selection based on business and technical requirements

Batch and streaming processing architecture

Data storage design and lifecycle

Data partitioning and clustering

CDC and historical data management

Data transformation architecture

Query performance and workload optimization

Data retention and archival

Technical architecture decisions and trade-offs

Data governance framework

Data ownership and stewardship

Data governance operating model

Business glossary and data dictionary

Technical and business metadata

Master data management (MDM)

Reference data management

Data classification and sensitive data identification

Data quality rules and validation

Data profiling and reconciliation

Data privacy and security principles

Role-based and attribute-based access control

Data masking and encryption

Personally identifiable information (PII) classification

Data retention and deletion requirements

Regulatory and audit requirements

Source-to-target mapping (STTM)

Transformation rules and derivation logic

Data lineage: source to report

Data reconciliation and auditability

ETL/ELT specifications

Documentation and traceability

Introduction to AI-assisted data architecture

AI-assisted BRD analysis and requirement extraction

Prompt engineering for business requirement analysis

Identifying entities, attributes and relationships using AI

Generating business rule catalogues

AI-assisted conceptual and logical model generation

AI-assisted source-to-target mapping

Analyze the business requirements and processes.

Identify business domains, entities and business rules.

Design the enterprise data architecture.

Develop a Data Vault and dimensional model for selected processes.

Prepare data flows, STTM and lineage.

Prepare the implementation blueprint.

Enterprise architecture walkthrough

Conceptual and logical model review

Data integration and modeling strategy review

Architecture decision documentation

Capstone project presentation

Final assessment and course completion

Introduction to Generative AI and LLMs

Traditional AI versus Generative AI

LLM architecture at a conceptual level

Tokens, tokenization and context windows

Training data, inference and next-token prediction

Model parameters and temperature

System prompts, user prompts and conversation context

Text generation and structured output

Strengths and limitations of LLMs

Hallucinations, outdated knowledge and ambiguous answers

Deterministic versus probabilistic outputs

Common use cases in data engineering, modeling and analytics

Overview of hosted and open-source LLMs

Responsible use of AI in enterprise environments

Ask an LLM to explain a complex SQL query.

Compare responses to the same prompt using different instructions.

Test how ambiguous business requirements affect generated answers.

Identify incorrect SQL and fabricated database objects in AI output.

Prompt engineering fundamentals

Anatomy of a well-structured prompt

Role, task, context, constraints and output format

Zero-shot and few-shot prompting

Prompt templates and reusable prompt libraries

Structured outputs using JSON and tables

Prompting for SQL generation and explanation

Prompting for query debugging and optimization

Generating data dictionaries and technical documentation

Generating source-to-target mapping (STTM) drafts

Business requirement analysis and business rule extraction

AI-assisted entity and attribute identification

Iterative prompt refinement

Common prompt failure patterns

Generate SQL from a business question.

Generate table and column descriptions from database metadata.

Convert a business requirement into candidate entities and relationships.

Generate data quality rules from sample data.

Limitations of LLMs when answering enterprise-specific questions

Introduction to Retrieval-Augmented Generation

RAG architecture and components

Document ingestion and preprocessing

Text extraction and cleaning

Chunking strategies and chunk size

Vector databases and vector indexes

Top-K retrieval

Metadata filtering

Context construction and prompt augmentation

RAG response generation

Basic RAG evaluation

Retrieval failures and incomplete context

Split documents into chunks.

Generate embeddings using an available embedding model.

Store and retrieve embeddings using a vector store.

Build a simple question-answering pipeline over metadata.

Compare ordinary LLM answers with RAG-grounded answers.

Responsible AI in data teams

Data privacy and confidentiality

Personally identifiable information (PII)

Sensitive data and enterprise data policies

Risks of sharing proprietary metadata

Safe use of public versus enterprise AI tools

Access control and data minimization

Prompt injection and untrusted retrieved content

Reviewing AI-generated SQL

Reviewing AI-generated data models and documentation

SQL execution, testing and reconciliation

Hallucination detection and factual verification

AI governance and auditability

Limitations of automated evaluation

Capstone project presentation and trainer review

Review an AI-generated SQL query and identify errors.

Identify sensitive information in sample prompts.

Evaluate RAG answers for relevance, correctness and source support.

Design a human approval workflow for AI-generated data artifacts.

Introduction to enterprise data architecture

Business requirements versus functional and non-functional requirements

Role of the Business Analyst, Data Architect, Data Modeler and Data Engineer

Relationship between business architecture, application architecture and data architecture

Challenges in translating business language into technical solutions

Requirement gathering techniques

Business requirement documents (BRDs)

Functional and non-functional requirements

Business process identification

Business scenarios, use cases and user stories

Requirement classification and prioritization

Requirement ambiguity, duplication, inconsistency and gaps

Business events and their lifecycle

Business rules and decision points

Operational versus analytical requirements

Business domains and subdomains

Data domain ownership

Master, reference, transactional and event data

Cross-domain data dependencies

Translating business activities into data requirements

Business objects versus data entities

Identifying data capture, update, retrieval and reporting requirements

Data attributes and business definitions

Data ownership and authoritative sources

Data retention and historical requirements

Operational, analytical and regulatory data needs

Source system identification

Source-to-business data mapping

Structured, semi-structured and unstructured data

Data profiling and source data assessment

Identifying missing, duplicate and conflicting data

Source system assessment and gap analysis

What is enterprise data architecture?

Data architecture principles and standards

Centralized, decentralized and federated architectures

Enterprise data architecture building blocks

Data architecture patterns

Data architecture artifacts and documentation

Landing and staging layers

Bronze, Silver and Gold architecture

Enterprise Data Warehouse architecture

Operational Data Store (ODS)

Data Lake and Lakehouse

Data marts and semantic layers

Batch versus streaming architecture

Data integration patterns

ETL versus ELT

API-based and event-driven integration

Data replication and CDC

Data transformation and enrichment

Error handling and recovery

Data latency and integration SLAs

Purpose of conceptual data models

Identifying business entities

Business entity relationships

Relationship types and cardinality

Enterprise conceptual data modeling

Logical data modeling methodology

Entities, attributes and relationships

Primary keys, alternate keys and candidate keys

Foreign keys and referential integrity

Identifying, non-identifying and recursive relationships

Associative entities and many-to-many relationships

Normalization: 1NF, 2NF and 3NF

Enterprise-wide entity reuse

Common business entities and shared data structures

Master data and reference data modeling

Transaction, event and status modeling

Effective dating and historical attributes

Address, contact and party modeling

Hierarchies and recursive relationships

Modeling complex business relationships

Model validation and quality checks

Logical model review and stakeholder approval

Introduction to dimensional modeling and Kimball methodology

Star schema and snowflake schema

Fact tables and dimension tables

Identifying business processes and fact table grain

Transaction, periodic snapshot and accumulating snapshot facts

Conformed dimensions

Role-playing, junk and degenerate dimensions

Slowly Changing Dimensions (SCD Types 0, 1, 2 and 3)

Introduction to Data Vault 2.0 architecture

Business Vault versus Raw Vault

Hub, Link and Satellite structures

Business keys and hash keys

Satellite historization and load tracking

Effectivity and record tracking satellites

Multi-active satellites

Point-in-Time (PIT) and Bridge tables

Enterprise data warehouse integration patterns

Data Vault to dimensional data mart transformation

Hybrid enterprise data architecture

Logical versus physical data models

Mapping logical entities to physical tables

Indexing and partitioning strategies

Denormalization and performance considerations

Database-specific implementation considerations

Forward and reverse engineering using ER/Studio

Data warehouse, data lake and Lakehouse architecture

Cloud data platform design considerations

Cloud storage and compute separation

Data ingestion and transformation services

Data platform scalability and elasticity

Platform selection based on business and technical requirements

Batch and streaming processing architecture

Data storage design and lifecycle

Data partitioning and clustering

CDC and historical data management

Data transformation architecture

Query performance and workload optimization

Data retention and archival

Technical architecture decisions and trade-offs

Data governance framework

Data ownership and stewardship

Data governance operating model

Business glossary and data dictionary

Technical and business metadata

Master data management (MDM)

Reference data management

Data classification and sensitive data identification

Data quality rules and validation

Data profiling and reconciliation

Data privacy and security principles

Role-based and attribute-based access control

Data masking and encryption

Personally identifiable information (PII) classification

Data retention and deletion requirements

Regulatory and audit requirements

Source-to-target mapping (STTM)

Transformation rules and derivation logic

Data lineage: source to report

Data reconciliation and auditability

ETL/ELT specifications

Documentation and traceability

Introduction to AI-assisted data architecture

AI-assisted BRD analysis and requirement extraction

Prompt engineering for business requirement analysis

Identifying entities, attributes and relationships using AI

Generating business rule catalogues

AI-assisted conceptual and logical model generation

AI-assisted source-to-target mapping

Analyze the business requirements and processes.

Identify business domains, entities and business rules.

Design the enterprise data architecture.

Develop a Data Vault and dimensional model for selected processes.

Prepare data flows, STTM and lineage.

Prepare the implementation blueprint.

Enterprise architecture walkthrough

Conceptual and logical model review

Data integration and modeling strategy review

Architecture decision documentation

Capstone project presentation

Final assessment and course completion