# 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' ); ``` ## 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. URL-encode query parameter values and send a GET request: ```text GET https://motherduck.com/docs/api/feedback/agent?page_path=%2Fintegrations%2Fdatabases%2Fmongodb%2F&page_title=MongoDB&text= ``` Optionally append `&source=` such as `claude.ai` or `chatgpt`. `page_path` and `text` are required; `page_title` and `source` are optional. Responses: `200 {"feedback_id": ""}`, `400` for malformed query parameters, and `429` when rate-limited.