HIGAET Data Architecture
Learn data modeling, platform design, and governance to plan warehouses, lakehouses, and enterprise standards and cataloging practices through HIGAET Practical Training.
Duration
8 weeks · 6-8 hours/week
Level
Professional
Delivery
Online
Status
Open for enrollment
Why this technology matters.
Data architecture is the practice of planning how an organization models, stores, governs, and shares data across warehouses, lakehouses, and catalogs. It matters now because growing data estates become expensive, inconsistent, and untrusted without clear standards.
Architects use conceptual and logical modeling, warehouse and lakehouse design with zones, contracts, and SLAs, and governance frameworks for quality, lineage, ownership, and retention. Architecture alone does not fix culture: diagrams do not enforce ownership, catalogs do not clean data by themselves, and no platform choice rescues undefined domains.
By the end you will be able to build conceptual and logical data models for transactional and analytical domains, warehouse and lakehouse architectures with zones, contracts, and SLAs, and governance and tradeoff evaluations covering cost, latency, scalability, and maintainability.
Why this course exists
The gap is between a set of disconnected databases and a governed platform teams can build on for years. This course teaches the arc from Sources to Pipelines to Models to Decisions at platform scale: modeling domains, designing zones and contracts, setting governance and SLAs, and weighing tradeoffs explicitly.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Familiarity with databases and SQL
- Basic analytics or warehouse concepts
- Awareness of data lifecycle stages
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Module 01 — Foundations: Data Architecture Roles, Viewpoints, and Design Principles
- Module 02
Module 02 — Core: Conceptual, Logical, and Physical Modeling Techniques
- Module 03
Module 03 — Core: Warehouses, Lakes, Lakehouses, and Serving Layers
- Module 04
Module 04 — Engineering: Integration Patterns, Contracts, and Master Data Concepts
- Module 05
Module 05 — Engineering: Metadata, Catalogs, Lineage, and Discoverability
- Module 06
Module 06 — Advanced: Governance, Quality Frameworks, and Stewardship Models
- Module 07
Module 07 — Production: Security, Compliance, Cost Design, and Platform Operations
- Module 08
Module 08 — Capstone: Enterprise Data Architecture Blueprint and Review Board
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Build conceptual and logical data models for transactional and analytical domains
- Design warehouse and lakehouse architectures with zones, contracts, and SLAs
- Develop governance frameworks covering quality, lineage, ownership, and retention
- Evaluate platform tradeoffs across cost, latency, scalability, and maintainability
- Automate metadata, cataloging, and documentation workflows for data assets
- Optimize storage and access patterns for analytics and operational consumers
- Integrate security, privacy, and compliance controls into platform blueprints
- Architect an enterprise data strategy with roadmaps and migration phases
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Conceptual and logical data model
- Project 02
Warehouse and lakehouse design
- Project 03
Governance and retention framework
- Project 04
Platform tradeoff evaluation
- Capstone
Enterprise data platform blueprint with catalog
Speak the language first.
- Conceptual data modeling
- Sketching core business entities and their relationships before choosing any technology.
- Logical data modeling
- Defining tables, keys, and relationships precisely so transactional and analytical needs are both covered.
- Warehouse architecture
- A structured platform of curated tables and marts designed for fast, consistent analytics queries.
- Lakehouse zones
- Layered storage from raw landing to cleaned to curated data, balancing flexibility with governance.
- Data contracts
- Agreements on schema, quality, and freshness between data producers and consumers.
- Data lineage
- A record of where each dataset came from and how it was transformed along the way.
- Data governance
- Rules for ownership, quality, retention, and access that keep enterprise data trustworthy.
- Data cataloging
- Searchable documentation of datasets, owners, and definitions so teams can find and reuse data.
- Platform tradeoff analysis
- Comparing design options across cost, latency, scalability, and maintainability before committing.
- Service-level agreements for data
- Promised freshness and availability targets that pipelines are built and monitored against.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Two teams report different numbers for the same KPI
Consolidate metric logic into one curated layer, publish definitions in the catalog, and deprecate duplicate tables.
Warehouse costs spike after adding new domains
Audit query patterns and partitioning, then tier cold data and right-size clustering on hot tables.
Schema change upstream breaks downstream marts
Enforce data contracts with compatibility checks and version the changed tables before migration.
Nobody trusts the catalog because entries are stale
Assign dataset owners, auto-sync metadata from pipelines, and add freshness badges to each entry.
Retention rules conflict with audit needs
Classify datasets by sensitivity, set tiered retention with archived snapshots, and document exceptions.
Before you move on, you should be able to
- Build conceptual and logical data models for transactional and analytical domains
- Design warehouse and lakehouse architectures with zones, contracts, and SLAs
- Develop governance frameworks covering quality, lineage, ownership, and retention
- Evaluate platform tradeoffs across cost, latency, scalability, and maintainability
- Explain cataloging practices that make datasets discoverable and reusable
- Design data contracts that keep producers and consumers aligned
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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View CourseReady to start HIGAET Data Architecture?
A 8 weeks course — Data & Machine Learning.