Tower Examples

Working example apps for Tower, each built around a common data problem. Every example is a complete Tower app (a few Python files plus a Towerfile) that you can clone, deploy, and run in minutes, then adapt to your own stack.

Quick start

Install the Tower CLI, sign in, and run the first example. It needs no secrets and no setup:

git clone https://github.com/tower/tower-examples.git
cd tower-examples/01-hello-world
tower login
tower deploy
tower run

Then pick the example below that matches the problem you're trying to solve.

Find your problem

Start here: zero setup

Example	The problem it solves	What you need
01-hello-world	See the whole Tower workflow — deploy, run, parameters, logs — in two minutes	Nothing
15-interactive-marimo-notebook	Serve an interactive Python notebook as a running app, no infra to host	Nothing

Load files into a warehouse

Example	The problem it solves	What you need
02-dlthub-s3-to-snowflake	CSVs land in S3 and need to end up in Snowflake, incrementally and reliably (dlt)	Snowflake credentials — the source bucket is public
03-dlthub-s3-to-motherduck	Same S3 ingestion problem, targeting MotherDuck (dlt)	MotherDuck token — the source bucket is public
16-sling-data	Replicate local/JSON data into Snowflake with a declarative YAML config (Sling)	Snowflake connection secret

Build a lakehouse on Apache Iceberg

These examples form a small end-to-end lakehouse: ingest → analyze → query → maintain. They share one prerequisite: an Iceberg catalog named default, but Tower hosts this catalog for you, so there is nothing external to sign up for. Deploying an example from the Tower app creates it in one click (the same screen can fill required secrets with Tower sandbox values for testing); on the CLI path, create it once in the Tower UI.

Example	The problem it solves	What you need
05-write-ticker-data-to-iceberg	Pull data from an external API on a schedule and land it in an Iceberg table (demo data: stock prices)	Tower-hosted catalog — no API keys
06-analyze-ticker-data-in-iceberg	Run LLM-assisted analysis over data already in your lakehouse	Tower-hosted catalog + inference key (or Tower's sandbox key in-app)
09-run-duckdb-queries-on-iceberg	Query Iceberg tables with plain SQL from DuckDB	Tower-hosted catalog + its REST credentials as secrets
11-trim-ticker-table	Enforce a retention window by deleting old rows from an Iceberg table	Tower-hosted catalog with data (run 05 first)
17-list-catalog-tables	Inspect what namespaces and tables exist in a catalog	Tower-hosted catalog
18-read-table-rows	Peek at the first rows of any Iceberg table	Tower-hosted catalog with data

Orchestrate many runs

Example	The problem it solves	What you need
08-fan-out-ticker-runs	Fan one job out into parallel runs and wait for them all (Tower run/wait)	Example 05 deployed

Put LLMs and agents to work on your data

Example	The problem it solves	What you need
07-deepseek-summarize-github	Feed operational data (GitHub issues) to an LLM and get an actionable recommendation back	Tower-hosted catalog + inference API key
13-ticker-update-agent	Deploy an AI agent that answers questions from your business data and keeps it fresh	Tower-hosted catalog + OPENAI_API_KEY (or Tower's sandbox key in-app)

Run dbt in production

Example	The problem it solves	What you need
14-dbt-core-ecommerce-analytics	Run a real dbt Core project (seed → build) as a deployable, schedulable app	DBT_PROFILE_YAML secret with your warehouse profile

How every example works

Each directory is a self-contained Tower app:

• Towerfile — declares the app: name, entrypoint script, source files, and runtime parameters.
• A Python script — ordinary Python; no framework to learn.
• pyproject.toml — dependencies, installed automatically at run time.

The workflow is always the same: tower deploy from the example's directory, then tower run (add --local to execute on your machine while still using Tower secrets and catalogs). Set secrets with tower secrets create; each example's README lists exactly which ones it needs.

Learn more

• Tower documentation — concepts, CLI reference, guides
• Tower Control — describe an app in natural language and let the agent build and deploy it
• tower.dev — what Tower is and who it's for