hotdata-dlt-destination

Load data into Hotdata managed databases using dlt — then read it back through the same pipeline.

dlt handles extraction, schema inference, and batching. This package is a native dlt destination (JobClientBase + WithStateSync + WithSqlClient) for the Hotdata side: it uploads each batch as Parquet, registers it with your managed database, syncs pipeline state so incremental sources resume, and exposes a server-side read API.

Highlights

• Native destination — nested/child tables, dlt internal columns (_dlt_id, _dlt_load_id), schema versioning, and load tracking, all preserved.
• Incremental resume — pipeline state is persisted in the managed database, so incremental sources pick up where they left off across runs.
• Read your data back — query loaded tables through pipeline.dataset() (pandas / Arrow / fluent SQL / ibis), server-side on Apache DataFusion. No Hotdata-specific code.
• In-place schema evolution — new tables and columns are added to an existing database without recreating it or moving data.
• Append / replace / merge (upsert) — standard dlt write dispositions, plus an insert-only combine.

Contents

• Requirements
• Install
• Quickstart
• Read your data back
• Feature support
• Configuration
• Write modes
• Multiple tables
• Verify a load
• Demo pipeline
• How it works
• Development
• Contributing
• License
• Resources

Requirements

• Python 3.11+
• A Hotdata workspace, an API key, and its workspace ID. Grab both from your Hotdata dashboard, or with the Hotdata CLI.

Install

pip install hotdata-dlt-destination
# or
uv add hotdata-dlt-destination

# with the live ibis backend (dataset().ibis()):
pip install "hotdata-dlt-destination[ibis]"

Quickstart

import dlt
from hotdata_dlt_destination import hotdata

@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(
        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.

hotdata supports nested/child tables, preserves dlt's internal columns (_dlt_id, _dlt_load_id), and persists schema-version, load, and pipeline-state tables in the managed database so incremental sources resume correctly across runs. If an existing managed database is missing a declared table on a later run, the table is added to it in place; existing tables and their data are left untouched.

Read your data back

The same pipeline object that writes can read, through dlt's standard dataset interface — no Hotdata-specific code, no database IDs, no hand-written SQL:

ds = pipeline.dataset()

ds.table("orders").df()      # whole table -> pandas.DataFrame
ds.table("orders").arrow()   # -> pyarrow.Table

# raw SQL
ds("SELECT customer, sum(total) AS spend FROM orders GROUP BY customer").df()

# fluent
ds.table("orders").select("id", "total").where("total > 50").order_by("total").limit(10).df()

Queries run server-side on Hotdata's Apache DataFusion engine (Postgres-compatible SQL). It's the same read API you'd use with the duckdb, postgres, or bigquery destinations — enabled because hotdata advertises dlt's SQL-client interface (WithSqlClient).

You can also author queries with ibis, two ways. dataset().table("orders").to_ibis() gives an ibis table that dlt compiles to SQL and runs through the same client. dataset().ibis() returns a live ibis.hotdata backend — ibis expressions and raw SQL run server-side and come back as pandas/Arrow. The live backend needs the [ibis] extra (see Install).

Feature support

Where hotdata stands against the dlt destination capability spec. ✅ supported · ⚠️ supported with caveats · ❌ not supported.

Write dispositions

Keys are fixed at table creation. A table's key is declared the first time it's created in the managed database. Changing a resource's primary_key on a later run does not update the server-side key. A table that already exists without a key (e.g. created before this feature) can't gain one in place — the connector detects the missing key and falls back to a client-side combine for merges, so loads don't break.

Merge strategies

Replace strategies

Keys & column hints

Loader file formats

Structure & lifecycle

Staging, transactions & identifiers

Configuration

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

• max_table_nesting (default 1000) — maximum nested/child-table depth.
• loader_parallelism_strategy (default sequential) — managed-database loads lock at the catalog level, so different tables in the same database can't load concurrently. Override only if you know your loads won't contend for the same database.

Write modes

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

dlt resources set write_disposition to append, replace, or merge only. merge performs upsert-by-primary-key — it is what the internal upsert disposition resolves to, so there is no separate upsert to set. The destination also implements an insert-only combine (insert rows whose key isn't already present, never updating existing rows), but dlt does not expose it as a resource write_disposition, so it cannot be selected per resource.

Set the default for all resources on the destination:

hotdata(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(
        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 — it's added to the existing database in place.

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 an 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 applies it with a native load mode

replace and append apply the uploaded batch directly. merge maps to a native upsert — the server matches rows by the table's declared key (from your resource's primary_key, declared on the table at setup) and updates the matches while inserting the rest — with no full-table read. Only insert-only, and a merge on a table without a resolvable primary_key, still read the current contents, combine in Python (falling back to dlt's _dlt_id), and replace. This is all transparent — your resource just yields rows.

The destination preserves dlt's native _dlt_id / _dlt_load_id columns and persists dlt's schema-version, load, and pipeline-state tables in the managed database so incremental sources can restore their state on the next run. No extra columns are added.

See docs/architecture.md and the runbook for the internals.

Development

The project uses uv for dependency management.

git clone https://github.com/hotdata-dev/hotdata-dlt-destination.git
cd hotdata-dlt-destination

uv sync                 # install deps (including dev group)

uv run pytest           # run the test suite
uv run ruff check       # lint
uv run ruff format      # format
uv run mypy             # strict type-check

The test suite runs entirely offline — end-to-end tests exercise real dlt pipelines through the destination against an in-memory backend, so no Hotdata credentials are needed.

Contributing

Issues and pull requests are welcome. Please:

• Open an issue to discuss substantial changes before starting.
• Keep the suite green (uv run pytest) and the lint/type checks clean (uv run ruff check, uv run mypy).
• Add a note to CHANGELOG.md under [Unreleased].

Release process is documented in RELEASING.md.

License

MIT © Hotdata Inc.

Resources

• Hotdata Python SDK
• hotdata-framework
• dlt documentation
• Architecture · Runbook · SQL-client spec
• Changelog