What metrics would you use to evaluate VAST Data's DASE (Disaggregated Shared Everything) architecture?

Introduction

Evaluating VAST Data's DASE (Disaggregated Shared Everything) architecture requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

VAST Data's DASE architecture is a revolutionary approach to data storage and management, designed to overcome the limitations of traditional storage systems. It disaggregates storage media and compute resources while allowing shared access to all data, aiming to provide unparalleled performance, scalability, and efficiency.

Key stakeholders include:

1. Enterprise IT departments: Seeking cost-effective, high-performance storage solutions
2. Data scientists and analysts: Requiring fast access to large datasets
3. Cloud service providers: Looking to optimize infrastructure costs and performance
4. VAST Data's engineering team: Responsible for ongoing development and improvement
5. Investors and company leadership: Focused on market adoption and financial performance

User flow typically involves:

1. Initial deployment and integration with existing infrastructure
2. Data migration and organization within the DASE system
3. Ongoing data access, analysis, and management tasks

DASE fits into VAST Data's strategy of disrupting the storage market by offering a unified solution that eliminates the need for tiered storage and provides AI-class infrastructure for all data. It competes with traditional SAN and NAS systems, as well as cloud-native storage solutions, by offering superior performance and cost-efficiency.

In terms of product lifecycle, DASE is in the growth stage, having gained initial traction but still working towards widespread adoption in the enterprise storage market.

Hardware considerations:

• Reliance on specific hardware components (e.g., NVMe SSDs, storage-class memory)
• Integration with various server and network infrastructures
• Scalability of physical deployments

Software considerations:

• Proprietary software stack for managing disaggregated resources
• APIs and integration points with popular data management and analytics tools
• On-premises and cloud deployment options