Spatial9 production solutions for Databricks, Snowflake, Vertex AI, Bedrock and open source

Production code, not samples: a Databricks Solutions Accelerator, Snowflake, Google Vertex AI and Amazon Bedrock agents, and an open source pipeline, all sharing one Python MCP and A2A client. Microsoft Fabric is not supported.

Spatial9 production solutions

Deployable code that runs governed catalog renders from the data and AI platforms enterprises already run, or from an all open source stack. Every solution follows the same rules: key in the platform's secret store, at most 3 renders at once, an idempotency_key per track and version, and results written back for audit.

Platform · Folder · Pattern

Databricks · databricks · Unity Catalog connection, Lakeflow Job, Volumes, optional AI Gateway MCP Services

Databricks Solutions Accelerator · databricks/solution-accelerator · Asset Bundle with Auto Loader, MCP, A2A and REST API routes, audit table and gold views

Snowflake · snowflake · External access integration, Python procedure, Task, Cortex Agent custom tool

Google Cloud and Vertex AI · vertex-ai · ADK agent with McpToolset, Agent Engine, Cloud Run job and Scheduler, BigQuery

AWS and Amazon Bedrock · bedrock · Strands agent, Lambda, Step Functions, S3, AgentCore Gateway

Open source stack · open-source · MinIO, Kafka, PostgreSQL, FFmpeg, FastAPI and Dagster with real-time A2A webhooks

Any Python runtime · shared/spatial9_mcp.py · The MCP handshake with requests only

Any Python runtime · shared/spatial9_a2a.py · Agent2Agent tasks: send, follow, cancel, webhooks

Other orchestrators such as Prefect, Salesforce Agentforce, ServiceNow and IBM watsonx Orchestrate can reuse shared/spatial9_mcp.py or connect to https://spatial9.ai/mcp as a remote MCP server where they support one.

Microsoft Fabric is not supported.

Guides:

Files

Read the enterprise guide