To inspect exported CSV or Parquet data in a graphical SQL editor, connect DBeaver to DuckDB and query the files directly—no production database or preliminary import is required. The DuckDB route is distinct from DBeaver’s standalone CSV and Parquet drivers, which handle complex queries differently.
Connect DBeaver to DuckDB
DuckDB’s DBeaver guide describes a connection using its JDBC driver. You can use an on-disk DuckDB database or choose :memory: for a temporary session; the guide specifically identifies the in-memory option as useful for querying Parquet files or testing. The steps below follow DuckDB’s guide; exact interface wording can vary between DBeaver versions.
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- Install and open DBeaver, then choose to create a new database connection.
- Select DuckDB from the database list.
- For the database path, enter a DuckDB database file path or
:memory:. - Test the connection. If DBeaver prompts you to download or install the DuckDB JDBC driver, allow it. If DBeaver cannot download it, DuckDB documents manual driver installation as an alternative.
- Open a SQL script for the connection and execute your queries.
DBeaver may need network access to download the JDBC driver. See the DuckDB DBeaver connection guide for its current setup instructions.
Inspect a CSV export
DuckDB can read a CSV file directly. For example, use a relative path from the database connection’s working context or an appropriate full path:
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SELECT *
FROM read_csv('exports/orders.csv')
LIMIT 20;When the filename extension identifies the format, DuckDB also supports the shorter form:
SELECT *
FROM 'exports/orders.csv'
LIMIT 20;Use read_csv when you need to specify reader options. CSV has no built-in structural metadata that guarantees column names and types, so check the inferred result before relying on it. Pay particular attention to dates, identifiers with leading zeros, delimiters, and strings that might be interpreted as nulls. Inference is a starting point, not a substitute for confirming what the values mean in your data.
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If you will query the same data repeatedly and want a table in the DuckDB database, you can materialize it with a statement such as:
CREATE TABLE orders AS
SELECT * FROM read_csv('exports/orders.csv');Creating a table is optional; it is not a prerequisite for querying the file. DuckDB also documents loading data into an existing table and using COPY as other loading options. See DuckDB’s CSV import documentation for reader options and loading details.
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Discover and query a Parquet file
Parquet stores columnar data with schema information. DuckDB can infer the format from the extension, so you can inspect a file and then select the columns and rows relevant to your question:
DESCRIBE SELECT * FROM 'exports/orders.parquet';
SELECT order_id, customer_id, total
FROM 'exports/orders.parquet'
WHERE total > 100
LIMIT 50;For a file whose extension does not identify it as Parquet, use read_parquet('path') explicitly. DuckDB documents both direct Parquet reads and inspection with DESCRIBE in its Parquet documentation.
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Read multiple Parquet exports
For files with compatible schemas, pass a list or use a glob. For example:
SELECT *
FROM read_parquet('exports/orders-*.parquet');A directory path can recursively read Parquet files below that directory. Use a glob such as exports/*.parquet when you mean files directly inside that directory rather than nested folders. DuckDB provides a virtual filename column by default starting in version 1.3.0, which can help trace rows to their source file. If files have schema differences, union_by_name is an available option; it is not enabled by default. Confirm how the schemas should be aligned before combining exports.
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Choose the right DBeaver route
There are two different ways to work with files in DBeaver. With a DuckDB connection, SQL runs in DuckDB and can use DuckDB’s CSV and Parquet readers. DBeaver also offers separate CSV and Parquet drivers that expose selected files through the IDE. Those file drivers are not the same as connecting to DuckDB.
| Workflow | How queries are handled | Useful distinction |
|---|---|---|
| DBeaver connected to DuckDB | DuckDB reads supported files through its own SQL engine. | Use DuckDB SQL features and readers, including direct CSV and Parquet queries. |
| DBeaver CSV or Parquet driver | DBeaver documents that simple queries read the source directly; the first complex query imports the whole file into a temporary internal SQLite database for advanced SQL functions. | DBeaver says later queries can be faster after import and documents an option to retain and reuse the processed database. |
The second route may suit a quick look through a file using DBeaver’s file-driver workflow. For a DuckDB-based investigation, create the DuckDB connection and query the source file as shown above. Read DBeaver’s documentation for the CSV driver and Parquet driver for their behavior and options.
What affects query performance
DuckDB documents automatic projection and filter pushdown for Parquet where applicable: selecting a few columns can avoid reading unrelated columns, and filters may skip data using Parquet statistics such as zonemaps. These are optimization opportunities, not a speed guarantee. Their effect depends on the file’s metadata and layout, as well as the query.
- Columns selected: requesting only needed columns gives projection pushdown an opportunity to reduce reading;
SELECT *asks for every column. - Filters and statistics: filter pushdown can help when Parquet zonemaps let DuckDB rule out data, but not every filter can skip row groups.
- File layout: file count, file sizes, compression, and row-group size all affect the work involved.
- Workflow: a DuckDB connection reads with DuckDB, while DBeaver’s separate file drivers document a whole-file import for the first complex query.
There is no single file-size threshold or speed multiplier established here. For exploratory work, start with a few columns and a selective filter, then adjust based on how your own files respond.
Quick Recap
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