Quick Start

Query any connected data source — from the terminal, a Python notebook, or inside an AI agent. Pick the integration that fits your workflow, then follow the steps below to authenticate and run your first query.

CLI

Python SDK

Fast path: create, load, and query

hotdata databases create \
  --name airbnb \
  --table listings

hotdata databases load airbnb.listings \
  --url https://hotdata.dev/data/sf-airbnb-listings.parquet

hotdata query \
  "SELECT COUNT(*) AS total_rows FROM default.public.listings" \
  --database airbnb

1) Install the CLI

Install walkthrough (YouTube):

brew install hotdata-dev/tap/cli

Verify the installation:

hotdata --help

2) Authenticate

Authenticate via browser:

hotdata auth

A browser window will open for you to sign in and authorize the CLI. Verify you're logged in:

hotdata auth status

3) Managed databases

Managed databases are Hotdata-owned catalogs you populate with parquet files. Create them on demand, load data, query immediately, and delete when done.

Managed database tutorial (YouTube):

Create a database and declare the tables you plan to load:

hotdata databases create \
  --name mydb \
  --table orders \
  --table customers

Load a parquet file from a local path or URL:

# From a local file
hotdata databases load mydb.orders \
  --file orders.parquet

# From a URL
hotdata databases load mydb.orders \
  --url https://hotdata.dev/data/sf-airbnb-listings.parquet

Query the loaded table — managed tables are always addressed as default.<schema>.<table>:

hotdata query \
  "SELECT * FROM default.public.orders LIMIT 10" \
  --database mydb

List databases and their tables:

hotdata databases list
hotdata databases tables mydb

Delete a table or the whole database when you're done:

hotdata databases tables delete --database mydb orders
hotdata databases delete mydb

4) Query your data

Basic query

hotdata query "SELECT 1 AS num" --database mydb

Optional flags: --database <name_or_id> to scope to a managed database, -o table|json|csv for output format.

Analytical functions

Window functions for rankings, running totals, and row comparisons:

hotdata query "
  SELECT id, amount,
    sum(amount) OVER (
      ORDER BY id
      ROWS BETWEEN UNBOUNDED PRECEDING
        AND CURRENT ROW
    ) AS running_total
  FROM default.public.orders
  LIMIT 10
" --database mydb

hotdata query "
  SELECT date, symbol, price,
    lag(price) OVER (
      PARTITION BY symbol
      ORDER BY date
    ) AS prev_price
  FROM default.public.stock_prices
" --database mydb

Full-text search

Requires a full-text index on the text column:

hotdata search "machine learning" \
  --table conn.public.articles \
  --column body \
  --select id,title,body \
  --limit 10

Vector search

Requires a vector index. The server resolves the embedding model from the index metadata:

Vector search demo (YouTube):

hotdata search "documents about machine learning" \
  --table conn.public.documents \
  --column body \
  --type vector \
  --limit 10

Equivalent SQL when you already have a query vector:

hotdata query "
  SELECT id, title,
    l2_distance(embedding, ARRAY[0.1, -0.2, 0.5]) AS dist
  FROM default.public.documents
  ORDER BY dist ASC LIMIT 10
" --database mydb

For cosine similarity, use cosine_distance; for inner product, use negative_dot_product.