Data Strategy

Data Platform requirements

1. Vision & Objectives

The company operates a SaaS platform for equity plan management. The data strategy must support:

• Internal analytics and ML use cases
• External, self-serve data access for clients
• Real-time data visibility and embedded analytics
• Strong data governance aligned with GDPR

Key objectives:

• Build a data mesh architecture with decentralized ownership
• Leverage Databricks (Spark, Delta Lake, Unity Catalog) as the core platform
• Enable real-time and batch data processing
• Provide self-service data access via APIs, BI tools, and direct SQL
• Ensure metadata management, lineage, and trust at scale

2. Core Data Domains (Mesh-Ready)

Domains should be business-aligned and reusable across other SaaS platforms:

1. Core Business Entities (e.g., EquityPlans, Grants, Products)
2. Users & Identity (e.g., Users, Permissions, Sessions)
3. Operational Activities (e.g., ClientOps, Billing, Support)
4. Usage & Engagement (e.g., Events, Interactions, Feature Usage)
5. Outcomes & Performance (e.g., KPIs, Adoption, Forecasts)
6. External Integrations (e.g., Valuation APIs, Compliance Feeds)

Each domain:

• Has clear ownership
• Exposes governed and discoverable datasets
• Publishes data contracts and SLAs

3. Ingestion & Processing Architecture

• Streaming-first approach for real-time pipelines
• Use Databricks Delta Live Tables for streaming + batch transformations
• Incorporate Kafka or similar message bus for event ingestion
• Partition data by tenant_id for multi-tenancy

4. Serving & Access Layers

Support for multi-modal data access:

1. API-based Access

   • Secure APIs to expose aggregated or filtered data
   • OAuth2 or key-based auth

2. Direct SQL Access

   • Unity Catalog + Databricks SQL for advanced clients
   • Row/column-level security enforced via policies

3. Embedded BI Tools

   • Power BI, Mode, or Metabase for user-friendly dashboards
   • Custom dashboards and drill-downs embedded in the SaaS UI

5. Multi-Tenancy & Access Control

• Logical multi-tenancy: one compute instance, shared data lake
• Partitioning by tenant_id
• Enforce row-level and column-level security using Unity Catalog
• Access control roles by persona (e.g., Internal PMs, External HR Admins, End Users)
• Dynamic field masking where needed

6. Governance & Compliance (GDPR-Aligned)

• Support for data erasure and subject access requests
• Audit trails for all access and transformation activities
• Clear data ownership and usage purpose metadata
• Ability to annotate PII fields and enforce retention/deletion policies
• EU data residency controls if required

7. Metadata Management & Discovery

Tooling must go beyond basic cataloging:

• Adopt DataHub for advanced metadata management

  • Column-level and job-level lineage
  • Domain-based organization and business glossary
  • User-friendly search and social discovery (e.g., tagging, usage ranking)

• Integrate with Unity Catalog as the foundation

• Allow annotations, ownership tagging, and feedback loops

8. Personas & Stakeholders

Internal:

• C-Suite (strategic dashboards, KPIs)
• Program & Product Managers (engagement, feature use)
• Data & AI Teams (full access for modeling, debugging)
• Engineers (lineage, data quality debugging)

External:

• Client Admins (HR, Finance, Legal): full organizational data
• End Users (employees): own equity data, projections
• Partners (tech clients): API or warehouse access

9. Tooling Stack (Initial Recommendation)

• Ingestion/Processing: Kafka + Databricks (DLT, Delta Lake)
• Orchestration: Databricks Workflows / Airflow (optional)
• Governance: Unity Catalog + DataHub (OSS or Acryl)
• Serving Layer: Databricks SQL, REST APIs, Embedded BI
• Security: Unity Catalog access policies, OAuth2, RBAC
• Monitoring & Auditing: Unity + custom audit logs

This requirements doc forms the blueprint for a scalable, governed, and self-serve data platform for SaaS businesses with real-time and compliance needs.