hotdata-dlt-destination 0.3.4

dlt destination for loading data into Hotdata managed databases.

hotdata-dlt-destination

Load data into Hotdata managed databases using dlt.

dlt handles extraction, schema inference, and batching. This package handles the Hotdata side — uploading each batch as Parquet and registering it with your managed database.

Install

pip install hotdata-dlt-destination

Quickstart

import dlt
from hotdata_dlt_destination import hotdata_destination

@dlt.resource(name="orders", write_disposition="append")
def orders_resource():
    yield [
        {"id": 1, "customer": "Alice", "total": 99.00},
        {"id": 2, "customer": "Bob",   "total": 49.50},
    ]

pipeline = dlt.pipeline(
    pipeline_name="my_pipeline",
    destination=hotdata_destination(
        database_name="sales",
        declared_tables=["orders"],
    ),
)

pipeline.run(orders_resource())

Set your credentials as environment variables before running:

export HOTDATA_API_KEY=your_api_key
export HOTDATA_WORKSPACE=your_workspace_id

That's it. On first run, the sales managed database is created automatically and the orders table is loaded.

Configuration

Parameter	Env variable	Default	Description
api_key	HOTDATA_API_KEY	required	Your Hotdata API key
workspace_id	HOTDATA_WORKSPACE	required	Your Hotdata workspace ID
database_name	HOTDATA_DATABASE	dlt	Managed database to load into
schema	HOTDATA_SCHEMA	public	Schema within the managed database
write_disposition	HOTDATA_WRITE_DISPOSITION	append	Default write mode (see below)
declared_tables	HOTDATA_DECLARED_TABLES	—	All table names the pipeline will write (required for multi-table pipelines — see below)
create_database_if_missing	—	True	Create the managed database if it doesn't exist yet
max_retries	HOTDATA_MAX_RETRIES	5	How many times to retry a failed request
retry_backoff_seconds	HOTDATA_RETRY_BACKOFF_SECONDS	1.0	Initial wait between retries (grows with each attempt)

You can pass any of these as keyword arguments to hotdata_destination(...), or set the corresponding environment variable.

Write modes

Each resource can control how its data lands in the table:

Mode	What it does
replace	Deletes everything in the table and loads the new batch. Good for full refreshes.
append	Adds new rows to the table without touching existing data. Good for event logs and immutable records.
merge (or upsert)	Updates existing rows by primary key, inserts new ones. Good for syncing a source of truth.

Set the default for all resources on the destination:

hotdata_destination(write_disposition="replace", ...)

Or set it per resource — this takes priority:

@dlt.resource(name="customers", write_disposition="merge", primary_key="id")
def customers_resource():
    ...

Multiple tables

When a pipeline writes to more than one table, pass all table names to declared_tables. Hotdata needs to know the full list upfront to set up the managed database correctly.

pipeline = dlt.pipeline(
    pipeline_name="ecommerce",
    destination=hotdata_destination(
        database_name="ecommerce",
        declared_tables=["customers", "orders", "products"],
    ),
)

pipeline.run([customers_resource(), orders_resource(), products_resource()])

If you add a new table later, include it in declared_tables on the next run.

Verify a load

After a pipeline runs, use the Hotdata CLI to check that the data landed:

# List your managed databases
hotdata databases list

# Check that tables are loaded and queryable
hotdata databases tables list --database sales

# Query the data
hotdata query "SELECT * FROM public.orders LIMIT 5" -d sales

Demo pipeline

The package includes a demo that downloads 9 macro-economic indicators from the Federal Reserve (FRED) and loads them into Hotdata. It's a good reference for how a real pipeline is structured.

export HOTDATA_API_KEY=your_api_key
export HOTDATA_WORKSPACE=your_workspace_id
uv run hotdata-dlt-demo

This creates a example_macro database with two tables:

• macro_indicators_raw — one row per (date, series, value), all 9 series at their original frequency
• macro_wide — one row per month from 1992 onward, each indicator as its own column

How it works

Each pipeline run:

1. dlt serializes your data to Parquet
2. The Parquet file is uploaded to Hotdata
3. load_managed_table replaces the target table with the new data

For append and merge, the destination reads the current table contents first, merges in Python, then writes the combined result back. This is done transparently — your resource just yields rows.

Every row gets two metadata columns added automatically:

• _hotdata_batch_key — identifies which pipeline run the row came from
• _hotdata_row_key — a stable hash of the row's content, useful for deduplication

Resources

• Hotdata Python SDK
• hotdata-runtime
• dlt documentation
• Architecture and runbook