Data

Key Takeaways

Data architecture decisions cascade through security, integration, and performance. Master data modeling trade-offs (lookup vs master-detail, normalization vs denormalization), LDV strategies (indexing, skinny tables, data skew mitigation), and migration planning. Every modeling choice has downstream consequences for sharing, query performance, and API payloads.

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

Practice

• Domain Grilling: D3 Data Q&A

Related Topics

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

Frequently Asked Questions

What data architecture topics does the CTA exam cover?

The CTA exam covers data modeling (object relationships, junction objects, polymorphic lookups, ERD patterns), large data volume strategies (indexing, skinny tables, data skew mitigation), data migration planning (tool selection, load sequencing, cutover strategies), data quality and governance (deduplication, MDM, retention policies), and external data access (Salesforce Connect, Data Cloud).

How is Data Architecture scored in the CTA review board?

Judges evaluate whether your data model supports the required sharing model, whether you have addressed LDV concerns with concrete strategies, whether your migration approach includes trial migrations and rollback plans, and whether you can defend relationship type choices (lookup vs master-detail) with clear reasoning about cascading impacts on security and deletion behavior.

What are the most common mistakes in Data Architecture during the CTA exam?

Candidates frequently fail by using master-detail relationships without considering the cascade delete and sharing implications, ignoring data skew in high-volume scenarios (account skew, ownership skew), proposing big-bang migration without a phased alternative, neglecting to address data archival for growing datasets, and not considering the query selectivity impact of their indexing strategy.

How should I handle large data volume scenarios in the CTA exam?

Start by identifying the data volumes mentioned in the scenario and mapping them against platform thresholds. Address indexing strategy (standard, custom, skinny tables), query selectivity for SOQL performance, data skew mitigation (especially account and ownership skew), archival approach (Big Objects, external storage, Data Cloud), and Batch Apex patterns for processing. Show that you understand the 2M+ record threshold where LDV strategies become critical.

When should I recommend Data Cloud vs Salesforce Connect for external data?

Recommend Data Cloud when the scenario requires identity resolution across systems, customer segmentation, calculated insights, or activation to marketing channels. Recommend Salesforce Connect (OData, cross-org adapter) when you need real-time access to external data without replication, the data volumes are moderate, and the external system has a stable API. Consider hybrid patterns when both real-time access and analytics are needed.