DuckLake is a new integrated data lake and catalog format from the DuckDB team. It uses a SQL database for all catalog and table metadata, and stores data in Apache Parquet files. Most users interact with DuckLake using SQL, but for those who prefer dataframes there’s Ibis—a portable Python dataframe library with support for DuckDB as a backend. To get started using DuckLake with Ibis, follow the steps below.

Create a new project with uv:

uv init --bare ducklake-ibis
cd ducklake-ibis

Install DuckDB, Ibis and JupyterLab:

uv add "ibis-framework[duckdb,examples]" jupyterlab

Launch JupyterLab:

uv run jupyter lab

Open localhost:8888 in your browser, create a new Python notebook, then incrementally add and run the code below:

import ibis

ibis.options.interactive = True

con = ibis.duckdb.connect(extensions="ducklake")
con.attach("ducklake:my_ducklake.ducklake")

con.list_catalogs()

['__ducklake_metadata_my_ducklake', 'memory', 'my_ducklake', 'system', 'temp']

con.raw_sql("USE my_ducklake")

<duckdb.duckdb.DuckDBPyConnection at 0x1068e1e30>

con.create_table(
    "penguins",
    ibis.examples.penguins.fetch().to_pyarrow(),
    overwrite=True
)

DatabaseTable: my_ducklake.main.penguins
  species           string
  island            string
  bill_length_mm    float64
  bill_depth_mm     float64
  flipper_length_mm int64
  body_mass_g       int64
  sex               string
  year              int64

con.list_tables()

['penguins']

penguins = con.table("penguins")
penguins

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

penguins.group_by("species").count()

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
┃ species   ┃ CountStar(penguins) ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
│ string    │ int64               │
├───────────┼─────────────────────┤
│ Gentoo    │                 124 │
│ Adelie    │                 152 │
│ Chinstrap │                  68 │
└───────────┴─────────────────────┘

con.raw_sql("USE __ducklake_metadata_my_ducklake")

<duckdb.duckdb.DuckDBPyConnection at 0x1068e1e30>

con.list_tables()

['ducklake_column',
 'ducklake_column_tag',
 'ducklake_data_file',
 'ducklake_delete_file',
 'ducklake_file_column_statistics',
 'ducklake_file_partition_value',
 'ducklake_files_scheduled_for_deletion',
 'ducklake_inlined_data_tables',
 'ducklake_metadata',
 'ducklake_partition_column',
 'ducklake_partition_info',
 'ducklake_schema',
 'ducklake_snapshot',
 'ducklake_snapshot_changes',
 'ducklake_table',
 'ducklake_table_column_stats',
 'ducklake_table_stats',
 'ducklake_tag',
 'ducklake_view']

con.table("ducklake_table_column_stats")

┏━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ table_id ┃ column_id ┃ contains_null ┃ contains_nan ┃ min_value ┃ max_value ┃
┡━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩
│ int64    │ int64     │ boolean       │ boolean      │ string    │ string    │
├──────────┼───────────┼───────────────┼──────────────┼───────────┼───────────┤
│        1 │         1 │ False         │ NULL         │ Adelie    │ Gentoo    │
│        1 │         2 │ False         │ NULL         │ Biscoe    │ Torgersen │
│        1 │         3 │ True          │ False        │ 32.1      │ 59.6      │
│        1 │         4 │ True          │ False        │ 13.1      │ 21.5      │
│        1 │         5 │ True          │ NULL         │ 172       │ 231       │
│        1 │         6 │ True          │ NULL         │ 2700      │ 6300      │
│        1 │         7 │ True          │ NULL         │ female    │ male      │
│        1 │         8 │ False         │ NULL         │ 2007      │ 2009      │
└──────────┴───────────┴───────────────┴──────────────┴───────────┴───────────┘

con.disconnect()