This repository serves as the official storage and distribution platform for Databricks Specialist Solutions Architect (SSA) content. It contains a comprehensive collection of technical resources, engagement materials, and structured content designed to support SSA activities and customer engagements.
This repository provides a centralized location for SSAs to access, share, and collaborate on official content including:
- Technical resources and documentation
- Engagement materials and templates
- Structured offerings and methodologies
- Best practices and reference implementations
- Training materials and knowledge sharing content
Some content in this repository includes structured "Offerings" - proactive, prescriptive engagement catalogs that focus on outcomes and optimize the ASQ process for driving momentum and results. These offerings are designed to:
- Accelerate the adoption of new technologies (e.g., Unity Catalog, GenAI)
- Expedite technical evaluation and competition (e.g., Databricks SQL, GenAI)
- Streamline positioning and hand-off to Professional Services and Partners
These Offerings should include a catalog-listing.yml file to promote discoverability with internal tooling.
This repository is organized into four main domains, each containing content focused on specific technology areas:
Purpose: Accelerate adoption of modern data warehousing technologies, expedite technical evaluation, and streamline positioning for data warehousing solutions.
Common Use Cases:
- Unity Catalog adoption and governance
- Databricks SQL optimization
- Data lakehouse architecture
- Performance tuning and optimization
- Migration strategies from traditional data warehouses
Purpose: Accelerate adoption of modern data engineering patterns, expedite technical evaluation of data platforms, and streamline positioning for data engineering solutions.
Common Use Cases:
- ETL/ELT pipeline optimization
- Data quality and governance
- Real-time data processing
- Data pipeline monitoring and observability
- Modern data stack architecture
- Apache Spark optimization
Purpose: Accelerate adoption of generative AI technologies, expedite technical evaluation of AI platforms, and streamline positioning for AI/ML solutions.
Common Use Cases:
- Large Language Model (LLM) integration
- Vector databases and embeddings
- RAG (Retrieval-Augmented Generation) patterns
- AI/ML model deployment and serving
- MLOps and model lifecycle management
- AI governance and responsible AI practices
Purpose: Accelerate adoption of security best practices, expedite technical evaluation of security solutions, and streamline positioning for cybersecurity implementations.
Common Use Cases:
- Data security and encryption
- Access control and identity management
- Compliance and governance frameworks
- Security monitoring and threat detection
- Data privacy and protection
- Security architecture and design patterns
Content in this repository follows consistent standards to ensure quality and effectiveness:
- Clear objectives and deliverables
- Well-documented and structured
- Minimal overhead requirements
- Prescriptive approach with structured methodology
- Browse the domain folders to find relevant content
- Use the templates in
/templatesfor creating new structured content - Ensure content aligns with SSA engagement principles
- Focus on measurable outcomes and clear value propositions
The /templates directory contains standardized templates and guidelines for creating consistent SSA content across all domains. Structured offerings should follow the template structure to maintain consistency and quality.
Databricks support doesn't cover this content. For questions or bugs, please open a GitHub issue and the team will help on a best effort basis.
© 2025 Databricks, Inc. All rights reserved. The source in this notebook is provided subject to the Databricks License [https://databricks.com/db-license-source]. All included or referenced third party libraries are subject to the licenses set forth below.
| library | description | license | source |
|---|