Data Vault 2.0 Automation: Metadata-Driven Framework Development

OBJECTIVE: Understand the principles, components and architectural layers of Data Vault 2.0. || TOPICS: Evolution of enterprise data warehousing | Data Vault 1.0 versus Data Vault 2.0. | Data Vault 2.0 architecture and its three pillars: modeling, methodology and architecture | Raw Vault, Business Vault and Information Marts | Data Vault in modern cloud data platforms | Data integration, scalability, auditability and agility || PRACTICAL EXCERCISES: Design a high-level Data Vault architecture for a banking enterprise || ASSESSMENT: Identify the responsibilities of each architectural layer.

OBJECTIVE: Translate business requirements and source system structures into Data Vault modeling requirements || TOPICS: Business processes, business rules and business concepts | Identifying business entities and their relationships | Business keys versus technical keys | Source system profiling and metadata analysis | Identifying master, transactional, reference and event data | Source-to-business concept mapping | Identifying data integration challenges | PRACTICAL EXCERCISES: Analyze customer, account and transaction source systems and identify candidate Hubs, Links and Satellites || ASSESSMENT: Prepare a business-to-source mapping document.

OBJECTIVE: Understand the core modeling principles and establish enterprise modeling standards | TOPICS: Business keys and their stability | Hash keys and hash differences | Hub, Link and Satellite fundamentals | Insert-only modeling and historical preservation | Naming conventions and modeling standards | Logical versus physical Data Vault models | Standard metadata and audit columns || PRACTICAL EXCERCISES: Create a basic logical model for customer and account data. || ASSESSMENT: Review the model for correct business key and object classification.

OBJECTIVE: Design enterprise Hubs using business keys and integrate multiple source systems. || TOPICS: Hub identification and design principles | Business key selection and standardization | Composite and concatenated business keys | Enterprise versus source-specific business keys | Hub relationships and business domains | Hub integration across multiple source systems | Hub metadata and audit attributes | Handling missing and invalid business keys || PRACTICAL EXCERCISES: Design Customer, Account, Product, Employee and Transaction Hubs for a financial services organization. || ASSESSMENT: Evaluate Hub design against enterprise modeling standards.

OBJECTIVE: Model relationships among enterprise business concepts using Links. || TOPICS: Link identification and design principles | Transactional and non-transactional Links | Standard, dependent-child and hierarchical relationships | Composite business relationships | Link cardinality and relationship constraints | Multi-source relationship integration | Link granularity and modeling decisions | Link naming and metadata standards || PRACTICAL EXCERCISES: Model customer-account, account-transaction, customer-product and employee-organization relationships. || ASSESSMENT: Validate Link grain, participating Hubs and business meaning.

OBJECTIVE: Design Satellites for descriptive attributes, history and source-specific data. || TOPICS: Satellite identification and design | Hub Satellites and Link Satellites | Source-aligned and business-aligned Satellites | Satellite splitting and grouping strategies | Attribute historization and change tracking | HashDiff design and change detection | Load date, record source and audit attributes | Satellite granularity and naming standards || PRACTICAL EXCERCISES: Design customer profile, account status, transaction details and customer risk Satellites. || ASSESSMENT: Review Satellite separation, historization and attribute placement.

OBJECTIVE: Apply advanced Data Vault structures to complex enterprise scenarios. | TOPICS: Multi-Active Satellites | Same-as Links and identity resolution | Hierarchical Links and recursive relationships | Non-Historized Links | Effectivity Satellites | Status tracking and relationship lifecycle | Dependent-child relationships | Choosing appropriate modeling patterns || PRACTICAL EXCERCISES: Model multiple customer addresses, account ownership changes, customer identity matching and organizational hierarchies. || ASSESSMENT: Select and justify modeling patterns for given business scenarios.

OBJECTIVE: Model historical changes, effective dates and temporal relationships. | TOPICS: Historical data preservation | Load time versus business effective time | Valid-time and transaction-time concepts | Late-arriving and out-of-order data | Retroactive corrections | Relationship effectivity | Handling deleted and deactivated records | Historical reconstruction and point-in-time analysis. || PRACTICAL EXCERCISES: Model customer address changes, account closures and backdated transaction corrections. || ASSESSMENT: Design a temporal model for a changing customer-account relationship.

OBJECTIVE: Build an integrated Raw Vault model from heterogeneous source systems. || TOPICS: Raw Vault principles and design standards | Source-aligned modeling | Multi-source data integration | Raw Vault object dependencies | Load sequencing and dependency management | Data lineage and traceability | Record source and audit requirements. || PRACTICAL EXCERCISES: Build a Raw Vault model using customer and account data from multiple banking systems. || ASSESSMENT: Review the complete Raw Vault logical model and its source traceability.

OBJECTIVE: Design Business Vault structures to implement reusable business rules and derived data. || TOPICS: Business Vault architecture and design principles | Raw versus derived business information | Business rules and transformation logic | Derived and computed Satellites | Business effectivity and calculated relationships | Business key resolution and survivorship | Rule versioning and historical recalculation | Reusable business logic. || PRACTICAL EXCERCISES: Design Business Vault objects for customer classification, account risk and consolidated customer identity. || ASSESSMENT: Classify business rules and map them to suitable Business Vault structures.

OBJECTIVE: Design PIT and Bridge structures to simplify point-in-time queries and complex relationship navigation. || TOPICS: Purpose and architecture of PIT tables | Snapshot, full-history, rolling and incremental PIT patterns | Satellite selection and effective record lookup | PIT refresh and maintenance strategies | Bridge table design and relationship traversal | Hierarchical and recursive Bridge tables | Performance and storage considerations | Virtual versus materialized access structures. || PRACTICAL EXCERCISES: Design a Customer PIT, Account PIT and customer-account Bridge for reporting requirements || ASSESSMENT: Select appropriate PIT and Bridge patterns for different query scenarios.

OBJECTIVE: Transform integrated Data Vault structures into reporting-ready dimensional models. || TOPICS: Information Mart architecture | Data Vault versus dimensional modeling | Identifying fact table grain | Transaction, periodic snapshot and accumulating snapshot facts | Dimension design and surrogate keys | Slowly Changing Dimensions | Handling many-to-many relationships | Mapping Hubs, Links and Satellites to dimensions and facts | Historical and current-state reporting. || PRACTICAL EXCERCISES: Design a banking dimensional model for account balances, transactions and customer activity using the Data Vault as the source. || ASSESSMENT: Validate fact grain, dimension relationships and historical reporting requirements.

OBJECTIVE: Convert logical Data Vault models into implementation-ready physical designs. || TOPICS: Logical-to-physical model transformation | Database-specific data types and naming standards | Primary keys, foreign keys and indexing strategies | Hash key and HashDiff implementation considerations | Partitioning and clustering strategies | Audit columns and technical metadata | Physical modeling in ER/Studio or ERwin | Platform-specific design considerations for Oracle, Snowflake and SQL Server || PRACTICAL EXCERCISES: Generate physical tables and DDL from selected logical Data Vault models. || ASSESSMENT: Review physical model consistency and implementation readiness.

OBJECTIVE: Establish enterprise-wide modeling standards, quality controls and governance practices. || TOPICS: Enterprise modeling standards and reusable patterns | Model naming and documentation conventions | Business glossary and metadata management | Model versioning and change management | Source-to-target mapping and data lineage | PII classification and data protection metadata | Data quality rules and model validation | Model review checklists and design governance || PRACTICAL EXCERCISES: Create a modeling standards document and perform a peer review of an enterprise Data Vault model. || ASSESSMENT: Conduct a structured model quality review.

OBJECTIVE: Apply the course concepts to design a complete enterprise Data Vault solution. || CASE STUDY: Global Financial Services Data Warehouse || PROJECT ACTIVITIES: 1. Analyze business requirements | 2. Identify business keys, entities and relationships | 3. Map source systems to enterprise business concepts | 4. Design the Raw Vault with Hubs, Links and Satellites | 5. Apply advanced modeling patterns | 6. Design Business Vault structures | 7. Design PIT and Bridge tables | 8. Develop dimensional Information Marts | 9. Prepare source-to-target mappings and data lineage | 10. Create model documentation. || DELIVERABLES: Enterprise Data Vault architecture diagram | Business process and source mapping | Complete logical Data Vault model || FINAL ASSESSMENT: Present and defend the enterprise model, explain key design decisions and respond to a technical model review.

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.