# MongoDB > Load MongoDB collections into MotherDuck with dlt, by exporting newline-delimited JSON, or through a managed connector, and flatten documents into columns. MongoDB stores documents rather than rows, so loading it into MotherDuck is as much a flattening problem as a transfer problem. There is no DuckDB `mongodb` extension, so collections come across as JSON, either through a tool that handles the schema work or through an export you read yourself. ## Load collections with dlt [dlt](/integrations/ingestion/dlt) has a MongoDB source and a MotherDuck destination, and it does the part you'd otherwise write by hand: it infers a schema from the documents, normalizes nested fields into columns and child tables, and evolves the schema as the documents change. ```bash dlt init mongodb motherduck pip install -r requirements.txt ``` Set the MongoDB connection string and your MotherDuck token, then run the pipeline: ```bash export MOTHERDUCK_TOKEN="" export SOURCES__MONGODB__CONNECTION_URL="mongodb+srv://:@/" python mongodb_pipeline.py ``` Configure which collections to load, and whether to load incrementally, in the generated pipeline script. See the [dlt MongoDB source documentation](https://dlthub.com/docs/dlt-ecosystem/verified-sources/mongodb) for the source options. ## Export JSON and read it For a one-time load or a small collection, export with `mongoexport` and read the file. The default output is one JSON document per line, which DuckDB reads natively: ```bash mongoexport \ --uri="mongodb+srv://:@/" \ --collection=orders \ --out=orders.json ``` ```sql CREATE TABLE orders AS SELECT * FROM read_json('orders.json', format = 'newline_delimited'); ``` DuckDB infers a schema from a sample of the documents, so nested objects become `STRUCT` columns and arrays become `LIST` columns. For a large export, write it to object storage and read from there instead of your local machine: ```sql CREATE TABLE orders AS SELECT * FROM read_json( 's3://my-bucket/mongo-export/orders/*.json', format = 'newline_delimited' ); ``` ## Load on a schedule with a Flight A [Flight](/concepts/flights) is Python that MotherDuck schedules and runs next to your data. Use one instead of running the export by hand when the load should repeat on a cron, retry on a transient failure, and keep a run history. There is no DuckDB `mongodb` extension, so run the dlt pipeline above inside the Flight and let dlt do the schema work: `MOTHERDUCK_TOKEN` is injected for you, so dlt's MotherDuck destination picks up the credential without configuration. The `mongodb` source is a dlt verified source, so vendor it alongside the Flight the same way `dlt init` lays it out: ```python import os import dlt import duckdb from sources.mongodb import mongodb DB = "mongo_ingest" def main(): os.environ.setdefault("HOME", "/tmp") os.environ["DESTINATION__MOTHERDUCK__CREDENTIALS__DATABASE"] = DB # dlt attaches the database but never creates it, so make sure it exists. duckdb.connect("md:").execute(f'CREATE DATABASE IF NOT EXISTS "{DB}"') pipeline = dlt.pipeline( pipeline_name="mongo_ingest", destination="motherduck", dataset_name="mongo_raw", ) source = mongodb( connection_url=os.environ["SOURCES__MONGODB__CONNECTION_URL"] ).with_resources("orders") print(pipeline.run(source, loader_file_format="parquet")) if __name__ == "__main__": main() ``` The MongoDB connection string contains a password, so keep it in a [Flight secret](/sql-reference/motherduck-sql-reference/create-secret#flight-secrets) rather than in the Flight's source or config. Each `PARAMS` key is injected into the run as an environment variable, which is what the code above reads: ```sql CREATE SECRET mongodb_creds IN MOTHERDUCK ( TYPE FLIGHTS, PARAMS MAP { 'SOURCES__MONGODB__CONNECTION_URL': 'mongodb+srv://:@/' } ); ``` Create the Flight with [`MD_CREATE_FLIGHT`](/sql-reference/motherduck-sql-reference/flights/md-create-flight), passing the Python above as `source_code`, the pinned dlt dependencies as `requirements_txt`, and `flight_secret_names := ['mongodb_creds']`. Leave `schedule_cron` off until a manual [`MD_RUN_FLIGHT`](/sql-reference/motherduck-sql-reference/flights/md-run-flight) succeeds, then add a schedule with [`MD_UPDATE_FLIGHT`](/sql-reference/motherduck-sql-reference/flights/md-update-flight). [Run a dlt ingest pipeline as a Flight](/cookbook/flight-dlt-ingest) is a config-driven version of this pattern, including the run ledger and the reason to prefer Parquet loader files. ## Use a managed connector If you want a scheduled sync without writing pipeline code, several MotherDuck ingestion partners list MongoDB as a source: [Airbyte](/integrations/ingestion/airbyte), [Fivetran](/integrations/ingestion/fivetran), [Estuary](/integrations/ingestion/estuary), and [Streamkap](/integrations/ingestion/streamkap). Streamkap and Estuary read MongoDB's change stream, so they suit change-data-capture rather than full reloads. ## Things to know - **Extended JSON leaks into your columns.** `mongoexport` writes BSON types as wrapper objects, so `_id` arrives as an object with an `$oid` key and dates as objects with a `$date` key. Project the values you want out of those wrappers after loading, or restrict the export with `--fields` to skip the types you don't need. dlt handles this conversion for you. - **Schema inference samples.** `read_json` infers types from the first documents it sees, so a field that only appears later, or changes type between documents, can be missed. Pass an explicit `columns` argument for a stable load, or set `union_by_name = true` when reading many files. - **Flatten before you query.** Querying `STRUCT` and `LIST` columns works, but downstream BI tools generally expect flat columns. Unnest the fields you report on into a curated table rather than making every consumer walk the document structure. - **MongoDB stays the write path.** MotherDuck is analytical. Keep application writes in MongoDB and treat MotherDuck as the read side for reporting. ## Related content - [dlt (data load tool)](/integrations/ingestion/dlt) - [JSON](/integrations/file-formats/json) - [Loading data from cloud storage or HTTPS](/key-tasks/loading-data-into-motherduck/loading-data-from-cloud-or-https) - [dlt MongoDB to MotherDuck guide](https://dlthub.com/docs/pipelines/mongodb/load-data-with-python-from-mongodb-to-motherduck) --- ## Docs feedback MotherDuck accepts optional user-submitted feedback about this page at `GET https://motherduck.com/docs/api/feedback/agent`. For agents and automated tools, feedback submission should be user-confirmed before sending. 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