Data

This domain covers data architecture, large data volume strategies, data modeling, and migration planning. Data decisions cascade through security (sharing model), integration (API payloads), and performance (query selectivity). Every modeling choice has downstream consequences.

Topics

Core Concepts

Data Modeling — Standard vs custom objects, relationships (lookup vs master-detail), junction objects, polymorphic lookups, external objects, Big Objects, record types, Person Accounts, formula fields, roll-up summaries, ERD patterns
Large Data Volumes — LDV thresholds, indexing (standard, custom, skinny tables), query selectivity, data skew (account, ownership, lookup), archival strategies, Batch Apex, Platform Cache
Data Migration — Migration phases, tools (Data Loader, Bulk API 2.0, Informatica, MuleSoft), load sequencing, External IDs, cutover strategies (big bang, phased, parallel), trial migrations, validation

Governance & Quality

Data Quality & Governance — Data profiling, deduplication (matching rules, duplicate rules), master data management, data lifecycle, retention policies, data stewardship, compliance (GDPR, data residency, Shield)
External Data — Salesforce Connect (OData, cross-org adapter), External Objects, Big Objects, Data Cloud, data virtualization vs replication, hybrid patterns
Data Cloud Architecture — Data Cloud (Data 360) deep dive: DSO/DLO/DMO hierarchy, identity resolution, calculated insights, segments, activation, zero-copy partner network, credit consumption model

Decision Frameworks

Decision Guides — Mermaid decision flowcharts for lookup vs master-detail, standard vs custom objects, archival strategy, migration approach, Person Accounts, normalized vs denormalized, virtualization vs ETL
Trade-offs — Normalization vs denormalization, on-platform vs external data, big bang vs phased migration, standard vs custom objects, lookup vs master-detail
Best Practices & Anti-Patterns — Organized by modeling, LDV, migration, quality, and governance with paired best practice and anti-pattern for each area

Objectives

Platform architecture considerations and optimization for large data volumes (LDV)
Data modeling concepts and database design implications
Data migration strategy, considerations, and appropriate tools
Data quality, governance, and compliance
External data access patterns and virtualization

Data architecture decisions ripple across the entire solution. These domains are most tightly coupled:

System Architecture — Data volume and LDV constraints directly affect org design and platform limits
Security — Data classification and sensitivity tiers drive field-level encryption and access control design; relationship type determines sharing model
Integration — Data migration and ETL pipelines require integration patterns and middleware tooling; external data access is an integration concern
Development Lifecycle — Data governance is part of organizational change management