Curriculum planned

Stage 04 · Warehousing & Analytical Databases

Data Warehousing and Dimensional Modeling

A complete data warehousing and dimensional modeling course covering analytical requirements, grain, facts and dimensions, star schemas, conformed dimensions and bus architecture, slowly changing dimensions, bridge/snapshot patterns, surrogate keys, source profiling, ETL/ELT, CDC, orchestration, physical/columnar design, semantic layers, marts, governance, testing, observability, performance, cloud/lakehouse architecture, migration, and production delivery.

Browse planned curriculum Back to all courses

30planned chapters

150reserved lesson paths

Intermediate → Advancedlearning level

Plannedcourse state

Coverage baselineVendor-neutral dimensional modeling and modern warehouse engineering principles spanning classic Kimball-style patterns, ELT/cloud warehouses, semantic metrics, governance, testing, and lakehouse coexistence

Course brief

Turn operational data into trustworthy analytical models by controlling grain, history, semantics, data quality, lineage, loading behavior, and physical performance.

This syllabus deliberately separates foundations, modeling, querying, internals, reliability, security, performance, operations, and production design so advanced material is not compressed into generic catch-all chapters.

By the end

You will be able to

Translate business processes and analytical questions into explicit grain, facts, dimensions, conformed models, and semantic metrics
Model history correctly with SCDs, snapshots, bridges, late-arriving data, surrogate keys, and temporal policies
Design reliable ETL/ELT and CDC pipelines with idempotency, data quality, reconciliation, orchestration, and observability
Choose physical warehouse patterns—partitioning, clustering, columnar storage, materialization, marts, and workload isolation—from measured workloads
Govern and operate analytical platforms with lineage, security, testing, cost/performance controls, DR, migration plans, and production SLAs

Complete planned syllabus

30 chapters · 150 lesson paths.

Every lesson path is reserved now but intentionally not linked until its lesson HTML is actually published. The sequence moves from foundations through advanced implementation, architecture, operations, reliability, security, tuning, and a production capstone.

Chapter 1

Data Warehouse Foundations: OLTP vs OLAP, Analytical Workloads, Architecture, and Lab Dataset

5 lessons

Operational vs Analytical Systems: Transaction Shape, History, Concurrency, Query Complexity, and Data FreshnessPlanned lesson · reserved path Chapter01/Lesson1.html

Planned02

Warehouse, Data Mart, ODS, Data Lake, Lakehouse, Semantic Layer, and BI Platform: Responsibilities and BoundariesPlanned lesson · reserved path Chapter01/Lesson2.html

Planned03

Classic Enterprise Warehouse, Dimensional Bus, Modern ELT, and Lakehouse-Centric Architectural StylesPlanned lesson · reserved path Chapter01/Lesson3.html

Planned04

Batch, Micro-Batch, Streaming/CDC, and Near-Real-Time Warehousing: Freshness vs Complexity TradeoffsPlanned lesson · reserved path Chapter01/Lesson4.html

Planned05

Define a Course Case Study with Source Systems, Business Processes, KPIs, SLAs, Security, and Data Quality ExpectationsPlanned lesson · reserved path Chapter01/Lesson5.html

Planned

Chapter 2

Requirements Engineering: Business Processes, Questions, Metrics, Dimensions, and Grain

5 lessons

Start from Decisions and Questions: Stakeholders, Use Cases, Reports, Explorations, and Data ProductsPlanned lesson · reserved path Chapter02/Lesson1.html

Planned02

Identify Business Processes and Events Before Designing TablesPlanned lesson · reserved path Chapter02/Lesson2.html

Planned03

Declare Grain in One Precise Sentence and Reject Mixed-Grain FactsPlanned lesson · reserved path Chapter02/Lesson3.html

Planned04

Separate Measures, Dimensions, Descriptive Attributes, Degenerate Dimensions, and Operational MetadataPlanned lesson · reserved path Chapter02/Lesson4.html

Planned05

Build a Requirements-to-Model Traceability Matrix Linking Every KPI and Slice to Source and GrainPlanned lesson · reserved path Chapter02/Lesson5.html

Planned

Chapter 3

Dimensional Modeling Foundations: Facts, Dimensions, Star Schemas, and Query Semantics

5 lessons

Why Star Schemas Optimize Understandability and Analytical Access PatternsPlanned lesson · reserved path Chapter03/Lesson1.html

Planned02

Fact Tables vs Dimension Tables: Keys, Measures, Descriptors, Width, and Growth BehaviorPlanned lesson · reserved path Chapter03/Lesson2.html

Planned03

Foreign-Key Relationships, Surrogate Keys, Referential Integrity, and Orphan HandlingPlanned lesson · reserved path Chapter03/Lesson3.html

Planned04

Star vs Snowflake vs Flat Wide Tables: Semantic, Performance, Maintenance, and Tooling TradeoffsPlanned lesson · reserved path Chapter03/Lesson4.html

Planned05

Model a Sales/Orders Process as a Star Schema and Validate Common BI Queries Against the Declared GrainPlanned lesson · reserved path Chapter03/Lesson5.html

Planned

Chapter 4

Fact Table Design: Additive, Semi-Additive, Non-Additive Measures, and Factless Facts

5 lessons

Additive Measures and Safe Summation Across DimensionsPlanned lesson · reserved path Chapter04/Lesson1.html

Planned02

Semi-Additive Balances/Snapshots: Why Time Often Requires Different AggregationPlanned lesson · reserved path Chapter04/Lesson2.html

Planned03

Ratios, Percentages, Averages, Distinct Counts, and Other Non-Additive MetricsPlanned lesson · reserved path Chapter04/Lesson3.html

Planned04

Factless Fact Tables for Coverage, Eligibility, Attendance, Conditions, and Event OccurrencePlanned lesson · reserved path Chapter04/Lesson4.html

Planned05

Audit a Metric Catalog for Double Counting, Incorrect Grain, and Unsafe AggregationPlanned lesson · reserved path Chapter04/Lesson5.html

Planned

Chapter 5

Dimension Design: Descriptive Context, Hierarchies, Attributes, and Analytical Usability

5 lessons

Dimension Attributes as Human-Readable Analytical Context, Not Operational Normalization ExercisesPlanned lesson · reserved path Chapter05/Lesson1.html

Planned02

Natural/Business Keys vs Surrogate Keys, Unknown Members, and Durable IdentityPlanned lesson · reserved path Chapter05/Lesson2.html

Planned03

Hierarchies, Levels, Ragged/Unbalanced Hierarchies, and Drill PathsPlanned lesson · reserved path Chapter05/Lesson3.html

Planned04

Wide Denormalized Dimensions vs Snowflaking, Outriggers, and Maintenance TradeoffsPlanned lesson · reserved path Chapter05/Lesson4.html

Planned05

Design a Customer/Product/Geography Dimension Set for Usability, History, and GovernancePlanned lesson · reserved path Chapter05/Lesson5.html

Planned

Chapter 6

Conformed Dimensions, Enterprise Bus Architecture, and Cross-Process Analytics

5 lessons

Conformed Dimensions and Facts: Shared Meaning Across Business ProcessesPlanned lesson · reserved path Chapter06/Lesson1.html

Planned02

Bus Matrix: Rows as Processes, Columns as Dimensions, and Incremental Warehouse DeliveryPlanned lesson · reserved path Chapter06/Lesson2.html

Planned03

Enterprise Definitions, Stewardship, Master/Reference Data, and Preventing Metric DriftPlanned lesson · reserved path Chapter06/Lesson3.html

Planned04

Shared Date/Product/Customer Dimensions Across Marts with Different GrainPlanned lesson · reserved path Chapter06/Lesson4.html

Planned05

Build a Bus Matrix and Identify Where Supposedly Shared Dimensions Are Not Actually ConformedPlanned lesson · reserved path Chapter06/Lesson5.html

Planned

Chapter 7

Slowly Changing Dimensions: Types 0–7, History, and Effective Dating

5 lessons

Type 0/1/2/3 Fundamentals: Preserve, Overwrite, Add Version Rows, or Carry Alternate ValuesPlanned lesson · reserved path Chapter07/Lesson1.html

Planned02

Type 2 Mechanics: Effective/Expiration Dates, Current Flag, Surrogate Keys, and Non-Overlapping WindowsPlanned lesson · reserved path Chapter07/Lesson2.html

Planned03

Hybrid SCD Patterns (Types 4–7), Mini-Dimensions, and Current-vs-Historical Attribute RequirementsPlanned lesson · reserved path Chapter07/Lesson3.html

Planned04

Detect Changes Reliably: Hashes, Column Comparison, Source CDC, Null Semantics, and Late CorrectionsPlanned lesson · reserved path Chapter07/Lesson4.html

Planned05

Implement and Test an SCD2 Load Including Repeat Runs, Backdated Changes, Deletes, and Unknown MembersPlanned lesson · reserved path Chapter07/Lesson5.html

Planned

Chapter 8

Special Dimension Patterns: Date/Time, Role-Playing, Junk, Degenerate, Mini, and Inferred Members

5 lessons

Date Dimension Rich Attributes, Fiscal Calendars, Holidays, Weeks, and Why Date Is More Than a TimestampPlanned lesson · reserved path Chapter08/Lesson1.html

Planned02

Role-Playing Dimensions for Order/Ship/Due Dates and Other Reused ContextPlanned lesson · reserved path Chapter08/Lesson2.html

Planned03

Junk Dimensions for Low-Cardinality Flags and Degenerate Dimensions for Transaction IdentifiersPlanned lesson · reserved path Chapter08/Lesson3.html

Planned04

Mini-Dimensions for Rapidly Changing Attributes and Profile/Segment HistoryPlanned lesson · reserved path Chapter08/Lesson4.html

Planned05

Inferred/Early-Arriving Dimension Members and Safe Completion When Source Details Arrive LaterPlanned lesson · reserved path Chapter08/Lesson5.html

Planned

Chapter 9

Bridge Tables and Many-to-Many Analytical Relationships

5 lessons

Why Direct Many-to-Many Joins Can Duplicate Facts and Corrupt MetricsPlanned lesson · reserved path Chapter09/Lesson1.html

Planned02

Bridge Tables for Multivalued Dimensions, Groups, Memberships, and HierarchiesPlanned lesson · reserved path Chapter09/Lesson2.html

Planned03

Weighting/Allocation Factors and When Allocated Measures Are Business Rules, Not Technical TricksPlanned lesson · reserved path Chapter09/Lesson3.html

Planned04

Temporal Membership in Bridges, Effective Dating, and Historical ReconstructionPlanned lesson · reserved path Chapter09/Lesson4.html

Planned05

Validate Many-to-Many Queries with Control Totals to Prove No Double CountingPlanned lesson · reserved path Chapter09/Lesson5.html

Planned

Chapter 10

Transaction, Periodic Snapshot, and Accumulating Snapshot Fact Tables

5 lessons

Transaction Facts for Atomic Events and Immutable Business ActivityPlanned lesson · reserved path Chapter10/Lesson1.html

Planned02

Periodic Snapshots for Inventory, Account Balances, Daily State, and Semi-Additive MetricsPlanned lesson · reserved path Chapter10/Lesson2.html

Planned03

Accumulating Snapshots for Pipeline/Workflow Milestones, Reopened Processes, and Updating RowsPlanned lesson · reserved path Chapter10/Lesson3.html

Planned04

Choosing Multiple Fact Tables for One Business Process Instead of Forcing One Universal GrainPlanned lesson · reserved path Chapter10/Lesson4.html

Planned05

Model an Order Fulfillment Process Using Transaction + Periodic + Accumulating Facts and Compare Questions SupportedPlanned lesson · reserved path Chapter10/Lesson5.html

Planned

Chapter 11

Surrogate Keys, Late-Arriving Facts/Dimensions, Deletes, Restatements, and History Corrections

5 lessons

Surrogate Key Assignment, Natural Key Reuse, Source-System Key Collisions, and Durable IdentityPlanned lesson · reserved path Chapter11/Lesson1.html

Planned02

Late-Arriving Facts: Resolve Dimension Version as of Event Time Without Assigning Current History IncorrectlyPlanned lesson · reserved path Chapter11/Lesson2.html

Planned03

Late-Arriving Dimension Changes, Backdated SCD2 Splits, and Historical Restatement PoliciesPlanned lesson · reserved path Chapter11/Lesson3.html

Planned04

Source Deletes, Soft Deletes, Inactivation, GDPR Erasure, and Analytical AuditabilityPlanned lesson · reserved path Chapter11/Lesson4.html

Planned05

Design a Correction Workflow that Preserves Reproducibility While Supporting Authorized RestatementsPlanned lesson · reserved path Chapter11/Lesson5.html

Planned

Chapter 12

Source System Profiling, Data Contracts, Lineage, and Ingestion Readiness

5 lessons

Inventory Sources, Owners, Extraction Methods, Change Mechanisms, SLAs, and Failure ModesPlanned lesson · reserved path Chapter12/Lesson1.html

Planned02

Profile Nulls, Uniqueness, Ranges, Distributions, Referential Integrity, Duplicates, and DriftPlanned lesson · reserved path Chapter12/Lesson2.html

Planned03

Source-to-Target Mapping Specifications, Transform Rules, Data Types, Units, Time Zones, and CodesPlanned lesson · reserved path Chapter12/Lesson3.html

Planned04

Data Contracts and Schema Change Detection: Compatible vs Breaking ChangesPlanned lesson · reserved path Chapter12/Lesson4.html

Planned05

Build a Source Readiness Report and Reject Ambiguous Semantics Before Pipeline DevelopmentPlanned lesson · reserved path Chapter12/Lesson5.html

Planned

Chapter 13

Data Quality Engineering: Validation, Standardization, Matching, Reconciliation, and Quarantine

5 lessons

Define Data Quality Dimensions: Completeness, Validity, Uniqueness, Consistency, Timeliness, and AccuracyPlanned lesson · reserved path Chapter13/Lesson1.html

Planned02

Standardize Names, Codes, Units, Time Zones, Encodings, and Reference DataPlanned lesson · reserved path Chapter13/Lesson2.html

Planned03

Deduplication and Entity Matching: Deterministic/Probabilistic Concepts and Stewardship BoundariesPlanned lesson · reserved path Chapter13/Lesson3.html

Planned04

Quarantine Bad Records, Preserve Raw Evidence, Repair/Reprocess, and Avoid Silent CoercionPlanned lesson · reserved path Chapter13/Lesson4.html

Planned05

Build Reconciliation Tests from Source Counts/Sums/Checksums to Warehouse Facts and DimensionsPlanned lesson · reserved path Chapter13/Lesson5.html

Planned

Chapter 14

ETL vs ELT Architecture: Staging, Raw, Integration, Presentation, and Transform Ownership

5 lessons

ETL vs ELT: Compute Location, Pushdown, Auditability, Cost, Lock-In, and Tooling TradeoffsPlanned lesson · reserved path Chapter14/Lesson1.html

Planned02

Landing/Raw/Staging/Integration/Presentation Layers and When Each Layer Adds ValuePlanned lesson · reserved path Chapter14/Lesson2.html

Planned03

Immutable Raw Data, Replayability, Audit Columns, Batch IDs, Load Timestamps, and LineagePlanned lesson · reserved path Chapter14/Lesson3.html

Planned04

Transformation Modularity, Reusable Intermediate Models, Dependency Graphs, and Environment PromotionPlanned lesson · reserved path Chapter14/Lesson4.html

Planned05

Design a Layered Pipeline for the Case Study and Justify Every Persisted Intermediate DatasetPlanned lesson · reserved path Chapter14/Lesson5.html

Planned

Chapter 15

Incremental Loading, CDC, Watermarks, High-Water Marks, and Idempotency

5 lessons

Full Refresh vs Incremental Loads: Volume, Freshness, Correctness, and Recovery TradeoffsPlanned lesson · reserved path Chapter15/Lesson1.html

Planned02

Timestamp/Sequence Watermarks, Overlap Windows, Clock Skew, Missing Rows, and Source Update SemanticsPlanned lesson · reserved path Chapter15/Lesson2.html

Planned03

Log-Based CDC: Inserts/Updates/Deletes, Before/After Images, Ordering, Transactions, and Schema EvolutionPlanned lesson · reserved path Chapter15/Lesson3.html

Planned04

Idempotent Merge/Upsert Patterns, Batch Identity, Deduplication, and Retry SafetyPlanned lesson · reserved path Chapter15/Lesson4.html

Planned05

Prove an Incremental Pipeline Handles Restart, Duplicate Delivery, Late Data, Deletes, and ReprocessingPlanned lesson · reserved path Chapter15/Lesson5.html

Planned

Chapter 16

Orchestration, Dependencies, Scheduling, Backfills, SLAs, and Failure Recovery

5 lessons

DAGs, Dependencies, Data Availability Sensors/Checks, and Avoiding Clock-Only SchedulingPlanned lesson · reserved path Chapter16/Lesson1.html

Planned02

Retries, Timeouts, Idempotent Tasks, Partial Failure, Compensation, and Operator InterventionPlanned lesson · reserved path Chapter16/Lesson2.html

Planned03

Partition-Aware Backfills, Historical Reprocessing, Resource Isolation, and Protecting Current LoadsPlanned lesson · reserved path Chapter16/Lesson3.html

Planned04

SLAs/SLOs for Freshness, Completeness, Duration, and Downstream AvailabilityPlanned lesson · reserved path Chapter16/Lesson4.html

Planned05

Design an Operational Runbook for a Failed Daily Load Including Root Cause, Repair, Replay, and CommunicationPlanned lesson · reserved path Chapter16/Lesson5.html

Planned

Chapter 17

Physical Warehouse Design: Schemas, Tables, Constraints, Partitioning, Clustering, and Distribution

5 lessons

Logical Dimensional Model vs Physical Implementation in Row Stores, Column Stores, and Cloud WarehousesPlanned lesson · reserved path Chapter17/Lesson1.html

Planned02

Schema/Database Organization, Naming, Ownership, Constraints, and Environment/Domain BoundariesPlanned lesson · reserved path Chapter17/Lesson2.html

Planned03

Partitioning by Date/Range/Hash: Pruning, Retention, Backfill, Small Partitions, and SkewPlanned lesson · reserved path Chapter17/Lesson3.html

Planned04

Clustering/Sort Keys, Distribution/Partition Keys, Co-Location, and Engine-Specific AlternativesPlanned lesson · reserved path Chapter17/Lesson4.html

Planned05

Create a Physical Design from Workload Evidence Rather Than Copying Generic Partition RecommendationsPlanned lesson · reserved path Chapter17/Lesson5.html

Planned

Chapter 18

Columnar Storage, Compression, Encoding, Vectorized Execution, and Analytical Scan Economics

5 lessons

Row vs Column Storage: Projection, Compression, CPU, Updates, and Analytical WorkloadsPlanned lesson · reserved path Chapter18/Lesson1.html

Planned02

Column Encodings, Dictionary/Run-Length/Delta-Like Compression Concepts and Data DistributionPlanned lesson · reserved path Chapter18/Lesson2.html

Planned03

Predicate/Projection Pushdown, Zone Maps/Min-Max Statistics, Partition Pruning, and Data SkippingPlanned lesson · reserved path Chapter18/Lesson3.html

Planned04

Vectorized Execution, Batch Processing, SIMD Concepts, and Why Analytical Engines Differ from OLTP DBMSsPlanned lesson · reserved path Chapter18/Lesson4.html

Planned05

Estimate Scan Bytes/CPU and Redesign Table Layout to Reduce Work Without Corrupting the Logical ModelPlanned lesson · reserved path Chapter18/Lesson5.html

Planned

Chapter 19

Materialized Views, Aggregate Tables, Cubes, and Precomputation

5 lessons

When Precomputation Beats Repeated Raw Fact Scans: Latency, Freshness, and Maintenance CostPlanned lesson · reserved path Chapter19/Lesson1.html

Planned02

Aggregate Fact Tables at Higher Grain and Semantic Rules for Reconciliation to Base FactsPlanned lesson · reserved path Chapter19/Lesson2.html

Planned03

Materialized View Refresh Strategies, Incremental Refresh Concepts, and Dependency ManagementPlanned lesson · reserved path Chapter19/Lesson3.html

Planned04

OLAP Cubes/Pre-Aggregation, Sparse Dimensional Combinations, and Modern Semantic AccelerationPlanned lesson · reserved path Chapter19/Lesson4.html

Planned05

Design an Acceleration Layer with Clear Freshness and Correctness ContractsPlanned lesson · reserved path Chapter19/Lesson5.html

Planned

Chapter 20

Semantic Layers, Metrics, Dimensions, Measures, and BI Contracts

5 lessons

Why a Warehouse Schema Alone Does Not Guarantee Consistent Business MetricsPlanned lesson · reserved path Chapter20/Lesson1.html

Planned02

Central Metric Definitions, Measures vs Metrics, Filters, Time Windows, Currency/Unit Semantics, and OwnershipPlanned lesson · reserved path Chapter20/Lesson2.html

Planned03

Semantic Models, Relationships, Drill Paths, Row-Level Security, and Tool IndependencePlanned lesson · reserved path Chapter20/Lesson3.html

Planned04

Metric Versioning, Deprecation, Tests, Documentation, and Preventing Dashboard-Specific Business LogicPlanned lesson · reserved path Chapter20/Lesson4.html

Planned05

Build a Governed Metric for Revenue/Active Customer/Retention and Trace It to Facts, Dimensions, and SourcesPlanned lesson · reserved path Chapter20/Lesson5.html

Planned

Chapter 21

Data Marts, Domain Data Products, Self-Service Analytics, and Ownership

5 lessons

Dependent vs Independent Marts and Why Independent Silos Break ConformancePlanned lesson · reserved path Chapter21/Lesson1.html

Planned02

Domain-Oriented Marts/Data Products with Shared Enterprise Dimensions and MetricsPlanned lesson · reserved path Chapter21/Lesson2.html

Planned03

Wide Reporting Tables vs Reusable Dimensional Marts: Speed, Duplication, and Semantic DriftPlanned lesson · reserved path Chapter21/Lesson3.html

Planned04

Self-Service Sandboxes, Certified Data, Promotion, Ownership, and Access BoundariesPlanned lesson · reserved path Chapter21/Lesson4.html

Planned05

Design a Marketing/Finance/Operations Mart Strategy that Reuses Conformed Data Without Blocking Domain DeliveryPlanned lesson · reserved path Chapter21/Lesson5.html

Planned

Chapter 22

Security, Privacy, Row/Column Policies, Masking, and Least-Privilege Analytics

5 lessons

Authentication, Roles, Groups, Service Identities, Separation of Duties, and Environment IsolationPlanned lesson · reserved path Chapter22/Lesson1.html

Planned02

Row-Level/Column-Level Security, Dynamic Masking, Tokenization, and Policy PlacementPlanned lesson · reserved path Chapter22/Lesson2.html

Planned03

PII Classification, Purpose Limitation, Retention, Consent, and Right-to-Delete in Historical WarehousesPlanned lesson · reserved path Chapter22/Lesson3.html

Planned04

Secure Views/Semantic Layer Policies vs Physical Data Access and Avoiding Bypass PathsPlanned lesson · reserved path Chapter22/Lesson4.html

Planned05

Threat-Model a Warehouse Including BI Exports, Shared Credentials, Staging Data, Backups, and Over-Broad Analyst RolesPlanned lesson · reserved path Chapter22/Lesson5.html

Planned

Chapter 23

Metadata, Data Catalogs, Lineage, Documentation, Ownership, and Governance

5 lessons

Technical vs Business Metadata, Data Dictionary, Schema Registry, Metric Catalog, and GlossaryPlanned lesson · reserved path Chapter23/Lesson1.html

Planned02

Column/Table/Job Lineage from Source to Dashboard and Impact Analysis for ChangesPlanned lesson · reserved path Chapter23/Lesson2.html

Planned03

Owners, Stewards, SLAs, Certification, Deprecation, and Data Product ContractsPlanned lesson · reserved path Chapter23/Lesson3.html

Planned04

Automated Documentation from Code/Models vs Human Business Context and ReviewPlanned lesson · reserved path Chapter23/Lesson4.html

Planned05

Build an Impact Analysis for a Source Column Change and Identify Every Model, Metric, Report, and Consumer at RiskPlanned lesson · reserved path Chapter23/Lesson5.html

Planned

Chapter 24

Testing Analytical Pipelines: Unit, Schema, Quality, Reconciliation, and Regression Tests

5 lessons

Schema/Contract Tests: Types, Nullability, Keys, Accepted Values, and RelationshipsPlanned lesson · reserved path Chapter24/Lesson1.html

Planned02

Data Quality/Business Tests: Invariants, Ranges, Freshness, Duplicates, and Referential IntegrityPlanned lesson · reserved path Chapter24/Lesson2.html

Planned03

SCD/Incremental/CDC Tests for History, Late Data, Deletes, Idempotency, and RestartPlanned lesson · reserved path Chapter24/Lesson3.html

Planned04

Metric/Reconciliation Tests Against Trusted Source Totals and Prior Period ExpectationsPlanned lesson · reserved path Chapter24/Lesson4.html

Planned05

Create CI + Production Data Tests with Severity, Quarantine, Alerting, and Waiver GovernancePlanned lesson · reserved path Chapter24/Lesson5.html

Planned

Chapter 25

Observability and Data Reliability: Freshness, Volume, Schema, Lineage, and Incident Response

5 lessons

Pipeline Metrics: Duration, Records/Bytes, Error/Reject Counts, Lag, Retries, and Resource UsePlanned lesson · reserved path Chapter25/Lesson1.html

Planned02

Data Observability: Freshness, Volume, Distribution, Schema, Lineage, and Quality ChangesPlanned lesson · reserved path Chapter25/Lesson2.html

Planned03

End-to-End Data SLOs from Source Availability to Certified Dashboard/Data ProductPlanned lesson · reserved path Chapter25/Lesson3.html

Planned04

Incident Triage: Source Failure vs Pipeline Bug vs Warehouse Performance vs Semantic ErrorPlanned lesson · reserved path Chapter25/Lesson4.html

Planned05

Build a Data Incident Runbook with Detection, Blast Radius, Consumer Communication, Repair, Backfill, and PostmortemPlanned lesson · reserved path Chapter25/Lesson5.html

Planned

Chapter 26

Performance Engineering and Workload Management: Scan Reduction, Joins, Aggregations, and Concurrency

5 lessons

Profile Representative BI/Ad-Hoc/ELT Workloads by Scan, Join, Shuffle, Spill, Queue, and Tail LatencyPlanned lesson · reserved path Chapter26/Lesson1.html

Planned02

Join Strategies and Dimensional Modeling Benefits: Broadcast/Hash/Merge Concepts, Cardinality, and SkewPlanned lesson · reserved path Chapter26/Lesson2.html

Planned03

Partition/Cluster Pruning, Predicate Pushdown, Materialization, Approximation, and Query RewritesPlanned lesson · reserved path Chapter26/Lesson3.html

Planned04

Workload Management, Queues/Warehouses/Resource Groups, Concurrency Scaling, and Noisy-Neighbor IsolationPlanned lesson · reserved path Chapter26/Lesson4.html

Planned05

Benchmark Before/After Changes and Track Performance Regression per Metric/Data ProductPlanned lesson · reserved path Chapter26/Lesson5.html

Planned

Chapter 27

Cloud Warehouses and Serverless Analytics: Separation of Storage/Compute, Elasticity, and Cost

5 lessons

Shared-Nothing vs Disaggregated Storage/Compute vs Serverless Warehouses: Architectural ConsequencesPlanned lesson · reserved path Chapter27/Lesson1.html

Planned02

Elastic Compute, Autoscaling, Concurrency, Pausing, Warehouses/Clusters, and Cold-Start TradeoffsPlanned lesson · reserved path Chapter27/Lesson2.html

Planned03

Object Storage, Columnar Formats, Metadata Services, Caching, and Remote Shuffle ConceptsPlanned lesson · reserved path Chapter27/Lesson3.html

Planned04

Cost Models: Scan/Compute/Credits/Slots/Storage/Egress and How Physical Design Changes SpendPlanned lesson · reserved path Chapter27/Lesson4.html

Planned05

Translate the Vendor-Neutral Model into a Cloud Warehouse and Document Which Optimizations Are Engine-SpecificPlanned lesson · reserved path Chapter27/Lesson5.html

Planned

Chapter 28

Lakehouse Coexistence: Open Table Formats, Medallion Layers, Warehouse Serving, and Federation

5 lessons

Warehouse vs Lakehouse: Governance, Transactions, Performance, Open Formats, and Ecosystem TradeoffsPlanned lesson · reserved path Chapter28/Lesson1.html

Planned02

Bronze/Silver/Gold Layering vs Dimensional Models: Complementary Concepts, Not Automatic ReplacementsPlanned lesson · reserved path Chapter28/Lesson2.html

Planned03

Apache Iceberg/Delta/Hudi Table-Format Concepts, Snapshot Metadata, Schema Evolution, and Warehouse InteroperabilityPlanned lesson · reserved path Chapter28/Lesson3.html

Planned04

Federated Query vs Data Movement, One-Copy Dreams, Performance, Consistency, and GovernancePlanned lesson · reserved path Chapter28/Lesson4.html

Planned05

Design a Hybrid Lakehouse+Warehouse Architecture with Clear System of Record, Serving, and Data-Movement BoundariesPlanned lesson · reserved path Chapter28/Lesson5.html

Planned

Chapter 29

Migration and Modernization: Legacy EDW, Mart Consolidation, Cloud Moves, and Semantic Preservation

5 lessons

Inventory Schemas, ETL Jobs, Reports, Stored Procedures, Dependencies, SLAs, and Hidden Business LogicPlanned lesson · reserved path Chapter29/Lesson1.html

Planned02

Rehost/Replatform/Refactor Strategies and Why Moving Tables Without Semantics Preserves Technical DebtPlanned lesson · reserved path Chapter29/Lesson2.html

Planned03

Parallel Run, Dual Loads, Historical Backfill, CDC Cutover, and Data ReconciliationPlanned lesson · reserved path Chapter29/Lesson3.html

Planned04

Preserve/Version Metrics and Semantic Behavior Across Old/New PlatformsPlanned lesson · reserved path Chapter29/Lesson4.html

Planned05

Build a Migration Acceptance Plan Covering Data, Query Results, Performance, Cost, Security, and RollbackPlanned lesson · reserved path Chapter29/Lesson5.html

Planned

Chapter 30

Production Capstone: Deliver a Governed, Testable, Performant Analytical Warehouse

5 lessons

Define Business Processes, Bus Matrix, Grain, Facts, Dimensions, Metrics, Source Contracts, SLAs, and Security RequirementsPlanned lesson · reserved path Chapter30/Lesson1.html

Planned02

Implement Dimensional Models, SCDs/Snapshots, Incremental/CDC Loads, Data Quality, Orchestration, and Semantic MetricsPlanned lesson · reserved path Chapter30/Lesson2.html

Planned03

Choose Physical Layout, Materialization, Marts, Workload Isolation, and Cloud/Lakehouse Integration from MeasurementsPlanned lesson · reserved path Chapter30/Lesson3.html

Planned04

Test, Reconcile, Load-Test, Secure, Observe, Backfill, Recover, and Execute a Data Incident/DR ExercisePlanned lesson · reserved path Chapter30/Lesson4.html

Planned05

Present the Warehouse as an Evidence-Based Data Product with Lineage, Costs, Known Limitations, Ownership, and Evolution RoadmapPlanned lesson · reserved path Chapter30/Lesson5.html

Planned