# **Technical Analysis: DuckDB as Datastore and API**

## **1, DuckDB Self-Hosted**

* **Description**: DuckDB can be run as a **self-hosted, server-side database** on disk.
* **Advantages**:
* Can handle **large datasets (GB–TB)** efficiently.
* Supports **SQL queries**, including complex joins, aggregations, and window functions.
* Works with **CSV, Parquet, and Arrow files**.
* **Limitations**:

* Single-node database; distributed queries require additional architecture.
* Requires a **backend service** to handle multi-user access and API exposure.

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## **2, DuckDB-Wasm**

* **Description**: DuckDB compiled to WebAssembly runs entirely in the browser.
* **Limitations for large datasets**:

* **Memory constraints**: Browsers typically cannot handle hundreds of MBs to TBs of data.
* **Single-user execution**: No concurrency or multi-user support.
* **No direct storage integration**: Cannot automatically access backend storage like S3/R2 for large CSVs.

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## ** Ingestion or CSV Import Requires Explicit Query**

* **Workflow**:

* CSVs are **not automatically ingested**.
* Data must be loaded explicitly using SQL:

    SELECT * FROM read_csv('test.csv', header=false);

* For repeated or automated ingestion:

* Must create **ETL pipeline** or scheduled job to read new files.
* Optionally persist data into a **DuckDB table** for faster queries:

    CREATE TABLE my_table AS SELECT * FROM read_csv('test.csv', header=false);

**Note**: DuckDB **does not watch storage for new files** for automatically ingestion .

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## **4️⃣ Exposing Data API Requires a Separate Backend**

* **Reason**: DuckDB is an **in-process database**, not a server.
* **API Setup**:

* Must implement a **backend service** (Node.js, Python, etc.) to:

* Accept HTTP/REST or GraphQL requests.
* Execute SQL queries against DuckDB.
* Return results to clients.
* **Implication**: Adds **operational overhead**, but allows multi-user access and integration with frontend applications.

### **Conclusion**

* **DuckDB self-hosted** is suitable as a lightweight, SQL-based datastore for moderate to large datasets but requires **backend services** for API exposure and automation.
* **DuckDB-Wasm** is **not suitable for TB-scale or multi-user scenarios**; ideal only for small-scale, client-side analytics.
* **Automatic CSV ingestion and API exposure** require **workflow orchestration**