Building & Governing Data Products in Starburst Enterprise
Design Secure, Reusable, and Governed Data Products at Scale
Duration
1 Day (4 hrs)
Level
Intermediate Level
Design and Tailor this course
Official Course
Overview
This course teaches learners how to design, build, and govern data products in Starburst Enterprise using the Enterprise Context Layer. Learners will move from raw data access to creating reusable, governed assets that enable consistent analytics and trusted AI outcomes.
Upon successful completion of this course, participants will be able to:
- Design data products using views & MVs strategically
- Implement domain-oriented data products in Starburst Enterprise
- Apply governance patterns for real-world use cases
- Use federation to compose cross-domain data products
- Create RBAC policies for table operations
- Define & exercise row-level and column-masking security rules
Audience
This course is primarily designed for data engineers and data platform architects.
Prerequisites
- Prior experience with Starburst web UI is desirable but not required.
- Intermediate SQL experience is assumed.
Curriculum
- Challenges with traditional data architectures (cost, fragmentation, governance gaps)
- The role of data products in AI readiness and enterprise scale
- Core characteristics of effective data products (discoverable, trustworthy, secure)
- Producer vs. consumer model
- Common failure patterns and success criteria
- Overview of the Enterprise Inteligence Platform
- Analytics Engine as the foundation for distributed data access
- Enterprise Context Layer as the backbone for governance and reuse
- Access without replatforming and governed-by-design architecture
- How Starburst reduces AI risk and enables scalable intelligence
- Domain-driven design and organizing data by business domains
- Structuring data products (schemas, datasets, domains)
- Best practices for naming conventions, metadata, and discoverability
- Defining ownership, governance boundaries, and usability standards
- Avoiding common data product anti-patterns
- End-to-end data product creation workflow
- Defining schemas, datasets, and business context
- Creating datasets using views and materialized views
- Adding metadata (tags, ownership, documentation)
- Publishing data products for discovery and reuse
- Conceptual and technical differences
- When to use views (real-time, semantic layer)
- When to use materialized views (performance optimization)
- Refresh strategies, storage considerations, and staleness management
- Best practices for naming, monitoring, and optimization
- Authentication vs. authorization in Starburst
- Supported authentication methods (LDAP, OAuth2, Okta, etc.)
- Authorization approaches (Built-in Access Control, Ranger, Immuta)
- Aligning access control with governance and compliance requirements
- Core RBAC concepts: roles, privileges, inheritance
- Configuring access at catalog, schema, table, and data product levels
- Assigning roles to users, groups, and other roles
- Validating and auditing access control policies
- Implementing row-level filtering using SQL expressions
- Applying column masking for sensitive data protection
- Execution behavior and performance considerations
- Managing policies across roles and data products
- Best practices for secure, scalable governance
Duration
1 Day (4 hrs)
Level
Intermediate Level
Design and Tailor this course
Official Course
