Salesforce Data 360 Under the Hood
Master the architecture of Salesforce Data 360, zero-copy federation, low-latency NVMe stores, and modern lakehouse ingestion patterns.
46 lessons · 3h 3m · Free · Taught by Cubz - Wiseman AI Founder
Open this course on Wiseman Academy →About this course
Salesforce Data 360 represents a fundamental shift in enterprise data architecture, bridging operational CRM data with analytical lakehouses. This course breaks down the core architectural principles, storage models, and compute choices necessary to build resilient, high-throughput data platforms within the Salesforce ecosystem.
You will follow a real-world enterprise scenario—tracing a loan application through every layer of the Data 360 stack. From storage abstractions and low-latency NVMe memory layers to change detection mechanisms and compute fabrics, you will gain deep insight into how data flows, transforms, and stays synchronized across systems.
Finally, we evaluate critical architectural tradeoffs, such as deciding whether to move data or query it in place. You will master zero-copy patterns, data federation, private connectivity, and ingestion connectors to minimize latency, reduce storage overhead, and maintain enterprise-grade governance.
What you'll learn
- Analyze the eight architectural trends driving the technical design of Salesforce Data 360.
- Trace data lifecycles across five layers of abstraction over object storage using an end-to-end loan application example.
- Design low-latency data access patterns utilizing NVMe storage layers between memory and lakehouse systems.
- Evaluate compute fabric choices, change detection mechanisms, and real-time processing pipelines.
- Determine when to ingest data versus executing zero-copy queries via federation and sharing.
- Configure secure ingestion patterns, native connectors, and private connectivity for Data 360 enterprise deployments.
Requirements
- Familiarity with enterprise data architecture concepts such as data lakes, object storage, and ETL/ELT pipelines.
- Working knowledge of Salesforce core platform data models and enterprise integration patterns.
- Basic understanding of SQL, data federation principles, and web API mechanics.
Who this course is for
- Salesforce Enterprise Architects and Solution Architects designing large-scale data and analytics platforms.
- Data Architects evaluating lakehouse integration, zero-copy data sharing, and real-time data engines.
- Senior Data Engineers and Integration Developers responsible for high-throughput enterprise data pipelines.
Curriculum
- Who This Course Is For, and What You'll Get (2:04)
- What Problem Does Data 360 Actually Solve? (2:21)
- Why Another Data Platform? Eight Trends Behind Data 360 (4:35)
- From Principles to Capabilities in Data 360 (5:35)
- One Loan Application Through Every Layer (4:20)
- Why Storage Comes First (2:00)
- Five Layers of Abstraction Over Object Storage (4:21)
- The Low-Latency Store: NVMe Between Memory and the Lakehouse (4:44)
- The Compute Fabric and Change Detection (5:34)
- Move It or Query It: The Central Choice (2:25)
- Connectors and Ingestion Patterns in Data 360 (5:22)
- Zero Copy: Federation, Sharing and Private Connectivity (5:06)
- From Raw Bytes to AI-Ready Data (2:40)
- From Raw Ingest to Harmonised: DLOs, DMOs and Data Spaces (5:16)
- Transformations, Unstructured Grounding and the Indexing Pipeline (5:29)
- Document AI in Data 360: Architecture, Pipelines and Constraints (5:43)
- Governance as a Control Plane (2:07)
- Data 360 Security Architecture: Substrate, Shared Responsibility and Identity (5:27)
- ABAC, Encryption and Observability in Data 360 (6:32)
- From Data In to Data Out (2:08)
- Outbound Activation and the Salesforce Integration Vocabulary
- MuleSoft, Informatica and the Resilience Question (5:33)
- Clean Rooms: Collaborating on Data You Cannot Share (2:24)
- Data Cloud Clean Rooms: Zero-Copy Collaboration Under Policy (5:47)
- Closing the Loop: What You Can Now Reason About (2:48)
- Provisioning and Interoperability Decisions (2:45)
- Before You Compare: Model and Criteria (2:17)
- The Data 360 Platform Model (4:34)
- Eleven Criteria for Choosing a Data 360 Home (4:34)
- Provisioning Decisions: What This Section Covers (2:10)
- Choosing the Home Org: Existing or Dedicated (4:28)
- · Data Cloud One: One Instance, Many Orgs (4:18)
- How Many Data 360 Orgs Do You Need? (5:21)
- Data 360-to-Data 360 Sharing: Federating Multiple Instances (4:30)
- · Provisioning Guidance and Anti-Patterns (5:28)
- How External Data Reaches Data 360 (2:22)
- Ingestion, Zero Copy, and the Hybrid Default (4:53)
- Ingestion Modes: Real-Time, Streaming, and Batch (4:29)
- Zero Copy in Depth: Live Query, Caching, and File Federation (5:33)
- The Hybrid Pattern: Anchoring and Federating with Numbers (4:44)
- Governance and the Interoperability Decision (5:16)
- Putting It Together: From Options to a Strategy (1:35)
- Three Archetypes, One Decision (4:40)
- Aurora Group: The Full Blueprint (4:28)
- The Data 360 Design Review Checklist (2:07)
- Course Wrap-Up: What You Can Now Decide (2:07)
Salesforce Data 360 Under the Hood — Wiseman Academy