Data 360 Architecture and Concepts

Data Cloud (Data 360)Data 360 Architecture and ConceptsWhy Salesforce built a data platform, how Data 360 (formerly Data Cloud) is architected as an Iceberg/Parquet lakehouse on Hyperforce, and how data flows from sources through DLOs and DMOs to unified profiles, insights, segments, activation, Agentforce and Tableau.Intermediate · about 60 minutesWhat you will learn

Explain why Salesforce needs a separate data platform next to the transactional CRM database

Trace the naming history from Customer 360 Audiences and Salesforce CDP to Data Cloud and Data 360

Describe the lakehouse architecture (Parquet, Apache Iceberg, Hyperforce) and its consequences for developers

Follow a record end-to-end through data streams, DLOs (__dll), DMOs (__dlm), identity resolution, calculated insights (__cio), segments and activation

Name the core Customer 360 Data Model objects and the purpose of data spaces

Distinguish zero-copy data federation from data sharing and pick the right one for a scenario

Understand home orgs, multi-org topologies and consumption-based billing at a high level

Contents

Why a data platform at all?

Naming history: from CDP to Data 360

Architecture: a lakehouse on Hyperforce

The core building blocks, end to end

The Customer 360 Data Model

Data spaces

Zero Copy: federation and sharing

Data 360 in your org topology

Pricing and consumption (high level)

Relationship to Agentforce and Tableau

Worked example: tracing one customer through the architecture

Vocabulary

Best practices

Common pitfalls

Hands-on exercises

Key takeaways