Getting started with Orbitra Data Apps

• Streamlit UI: Build interactive interfaces with Python-only code.
• Orbitra Environment Integration: Use get_lake_client() to create an OrbitraLakeClient with Orbitra Commons-managed authentication, and connect your app to Orbitra Flows scheduled pipelines.
• Dev Container: Reproducible environment with uv package manager and tooling.
• CI/CD Pipeline: Automated builds and deployments to Azure Container Apps pre-configured.

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Getting Started

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Step 1: Set Up Your Development Environment

# From project root
uv sync --all-extras

# (optional) activate venv if you created one
source .venv/bin/activate

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Step 2: Run the App Locally

streamlit run main.py --server.port 8501 --server.address 0.0.0.0

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Understanding the App

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Minimal Example

from orbitra.lake import get_lake_client
from orbitra.lake.client import OrbitraLakeClient
import streamlit as st

@st.cache_data
def load_dash_data() -> list[str]:
    client: OrbitraLakeClient = get_lake_client()
    namespaces = client.list_namespaces()
    return namespaces

with st.sidebar:
    st.title("Orbitra Dapps")
    if st.button("Clear Cache"):
        st.cache_data.clear()

namespaces = load_dash_data()
st.title("Orbitra Namespaces")
st.write(namespaces)

• @st.cache_data: Cache expensive operations like data fetches
• st.cache_data.clear(): Invalidate cached results when users need a fresh read from Orbitra Lake
• get_lake_client(): Access Orbitra Lake without manual authentication
• Sidebar: Navigation and controls

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Extended Streamlit Patterns

from concurrent.futures import ThreadPoolExecutor
from orbitra.lake import get_lake_client
from orbitra.lake.client import OrbitraLakeClient
import streamlit as st

client: OrbitraLakeClient = get_lake_client()
namespaces = client.list_namespaces()

selected_namespace = st.selectbox("Namespace", namespaces)
search = st.text_input("Search tables")

if st.button("Refresh"):
    st.cache_data.clear()
    st.rerun()

def get_tables(namespace: str) -> list[str]:
    return client.list_tables(namespace=namespace)

def get_table_count(namespace: str) -> int:
    return len(client.list_tables(namespace=namespace))

with st.spinner("Loading tables in parallel..."):
    with ThreadPoolExecutor(max_workers=2) as executor:
        tables_future = executor.submit(get_tables, selected_namespace)
        count_future = executor.submit(get_table_count, selected_namespace)
        tables = tables_future.result()
        total_tables = count_future.result()

filtered_tables = [t for t in tables if search.lower() in t.lower()]
st.caption(f"Total tables in namespace: {total_tables}")
st.metric("Table count", len(filtered_tables))
st.dataframe(filtered_tables)

• st.button("Refresh"): Trigger manual reloads for user-driven interactions
• st.cache_data.clear(): Clear memorized Streamlit data before rerunning so the UI fetches the latest values
• st.selectbox("Namespace", namespaces): Let users choose a value from loaded data
• st.text_input("Search"): Capture user text filters before querying or rendering
• st.metric("Table count", len(filtered_tables)): Highlight key KPIs at a glance
• st.dataframe(data): Render tabular results with built-in sorting and scrolling
• st.spinner("Loading data..."): Show progress feedback during slower operations
• concurrent.futures.ThreadPoolExecutor: Execute independent data reads in parallel for better responsiveness

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Development Workflow

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Common Tasks

• Edit main.py or create new modules
• Use Orbitra Lake for data access (see Lake Tutorial)
• Run locally: Use VS Code “Python: Streamlit” debug config or streamlit run main.py
• Clear cache: Use the sidebar button or call st.cache_data.clear()
• Format/lint: Ruff is pre-configured in the dev container. Run uvx ruff format to fix formatting issues

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Deployment

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CI/CD Pipeline

1. Builds a Docker image from .pipelines/Dockerfile
2. Pushes the image to your Azure Container Registry (ACR)
3. Deploys to Azure Container Apps with the new image tag

• Commit and push to the main branch
• The pipeline triggers automatically
• Monitor progress in Azure DevOps
• View logs and status in Azure Portal

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Next Steps

• Build your app: Add pages and components to main.py or organize into modules
• Connect to data: Use Orbitra Lake for data access
• Deploy: Commit and push to your default branch to trigger the pipeline