📄️ Develop apps
In Tower, you will be developing data applications, or “apps” for short. All source code-based data engineering artifacts - ETL/ELT pipelines, batch inference jobs, scripts, tasks of a larger DAG - are “apps” in Tower’s parlance. If you wondered what other data engineering artifacts there are, the answer is: "Tables! Data Sets!".
📄️ Test apps
Once you have prepared a Towerfile and changed your app code to receive secrets and parameters, you are ready to test the app. We recommend starting with a python run, then a local Tower run, and then a run in the Tower cloud.
📄️ Orchestrate apps
Orchestration in Tower gives you control over when and how your apps execute, allowing you to automate and compose more complex flows.
📄️ Observe and improve
Tower's observability features help you monitor the health of your data system, quickly identify issues, and take targeted action where needed. This guide explains how to use Tower's observability tools to maintain optimal performance.
📄️ Install and run Self-Hosted runners
This guide covers installing, configuring, and operating the Tower Runner on your own infrastructure across Linux, macOS, Windows, and Docker.
📄️ Working with Tables
This guide demonstrates Tower's table capabilities using two example apps:
📄️ Working with Models
Using Models for Inference Tasks
📄️ Advanced use cases
Programmatically determining the environment
📄️ Working in teams
Teams in Tower enable groups of users to collaboratively develop and run apps. By creating a team, you establish a shared workspace where multiple people can access the same Apps, Secrets, and resources.
📄️ Webhooks
Introduction
📄️ Tower MCP Server - from Zero to App
This walkthrough uses SSE transport, which requires running the MCP server as a background process. The Tower CLI now supports stdio transport (recommended), which is simpler to set up - your AI tool can spawn the server automatically. See the MCP Server Reference for configuration instructions.