https://github.com/databricks-solutions/specialist-offerings
https://github.com/databricks-solutions/specialist-offerings
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/databricks-solutions/specialist-offerings
- Owner: databricks-solutions
- License: other
- Created: 2025-10-01T05:39:53.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-12-19T23:12:14.000Z (5 months ago)
- Last Synced: 2025-12-22T10:17:49.669Z (5 months ago)
- Language: Python
- Size: 60.5 KB
- Stars: 0
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.md
- Codeowners: CODEOWNERS.txt
- Security: SECURITY.md
- Notice: NOTICE.md
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README
# Databricks Specialist Solutions Architect Content Repository
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.
## Repository Purpose
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.
## Repository Structure
This repository is organized into four main domains, each containing content focused on specific technology areas:
### 📊 Data Warehousing (`/data-warehousing`)
**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
### 🔧 Data Engineering (`/data-engineering`)
**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
### 🤖 GenAI (`/gen-ai`)
**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
### 🔒 Cybersecurity (`/cybersecurity`)
**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 Standards
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**
## Getting Started
1. Browse the domain folders to find relevant content
2. Use the templates in `/templates` for creating new structured content
3. Ensure content aligns with SSA engagement principles
4. Focus on measurable outcomes and clear value propositions
## Templates
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.
## How to get help
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.
## License
© 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 |
|----------------------------------------|-------------------------|------------|-----------------------------------------------------|