🗼 Laketower

Oversee your lakehouse

Utility application to explore and manage tables in your data lakehouse, especially tailored for data pipelines local development.

Features

Delta Lake table format support
Remote tables support (S3, ADLS)
Inspect table metadata
Inspect table schema
Inspect table history
Get table statistics
Import data into a table from CSV files
View table content with a simple query builder
Query all registered tables with DuckDB SQL dialect
Execute saved queries
Export query results to CSV files
Static and versionable YAML configuration
Web application
CLI application

Installation

Using pip (or any other Python package manager):

pip install laketower

Using uvx:

uvx laketower

Usage

Configuration

Laketower configuration is based on a static YAML configuration file allowing to:

List all tables to be registered

Format:

settings:
  max_query_rows: 1000
  web:
    hide_tables: false

tables:
  - name: <table_name>
    uri: <local path to table>
    format: {delta}

queries:
  - name: <query_name>
    title: <Query name>
    description: <Query description>
    totals_row: true
    parameters:
      <param_name_1>:
        default: <default_value>
    sql: <sql expression>

Current limitations:

tables.uri:
- Local paths are supported (./path/to/table, /abs/path/to/table, file:///abs/path/to/table)
- Remote paths to S3 (s3://<bucket>/<path>) and ADLS (abfss://<container>/<path>)
tables.format: only delta is allowed

Example from the provided demo:

tables:
  - name: sample_table
    uri: demo/sample_table
    format: delta
  - name: weather
    uri: demo/weather
    format: delta

queries:
  - name: all_data
    title: All data
    sql: |
      select
        sample_table.*,
        weather.*
      from
        sample_table,
        weather
      limit 10
  - name: daily_avg_temperature
    title: Daily average temperature
    sql: |
      select
        date_trunc('day', time) as day,
        round(avg(temperature_2m)) as avg_temperature
      from
        weather
      group by
        day
      order by
        day asc

Support for environment variables substitution is also supported within the YAML configuration using a object containing a single key env with the name of the environment variable to be injected. The value of the variable can contain JSON and will be decoded in a best effort manner (default to string value). For instance:

# export TABLE_URI=path/to/table

tables:
  - name: sample_table
    uri:
      env: TABLE_URI
    format: delta

Remote S3 Tables

Configuring S3 tables (AWS, MinIO, Cloudflare R2):

tables:
  - name: delta_table_s3
    uri: s3://<bucket>/path/to/table
    format: delta
    connection:
      s3:
        s3_access_key_id: access-key-id
        s3_secret_access_key: secret-access-key
        s3_region: s3-region
        s3_endpoint_url: http://s3.domain.com
        s3_allow_http: false

Depending on your object storage location and configuration, one might have to set part or all the available connection.s3 parameters. The only required ones are s3_access_key_id and s3_secret_access_key.

Also as a security best practice, it is best not to write secrets directly in static configuration files, so one can use environment variables to all dynamic substitution, e.g.

tables:
  - name: delta_table_s3
    uri: s3://<bucket>/path/to/table
    format: delta
    connection:
      s3:
        s3_access_key_id: access-key-id
        s3_secret_access_key:
          env: S3_SECRET_ACCESS_KEY
        s3_region: s3-region
        s3_endpoint_url: http://s3.domain.com
        s3_allow_http: false

Remote ADLS Tables

Configuring Azure ADLS tables:

tables:
  - name: delta_table_adls
    uri: abfss://<container>/path/to/table
    format: delta
    connection:
      adls:
        adls_account_name: adls-account-name
        adls_access_key: adls-access-key
        adls_sas_key: adls-sas-key
        adls_tenant_id: adls-tenant-id
        adls_client_id: adls-client-id
        adls_client_secret: adls-client-secret
        azure_msi_endpoint: https://msi.azure.com
        use_azure_cli: false

Depending on your object storage location and configuration, one might have to set part or all the available connection.adls parameters. The only required one is adls_account_name.

Also as a security best practice, it is best not to write secrets directly in static configuration files, so one can use environment variables to all dynamic substitution, e.g.

tables:
  - name: delta_table_adls
    uri: abfss://<container>/path/to/table
    format: delta
    connection:
      adls:
        adls_account_name: adls-account-name
        adls_access_key:
          env: ADLS_ACCESS_KEY

Web Application

The easiest way to get started is to launch the Laketower web application:

$ laketower -c demo/laketower.yml web

By default, the web application will run on host 127.0.0.1 and port 8000. If some custom setup is required (especially for cloud deployment), this configuration can be customized at runtime:

$ laketower -c demo/laketower.yml web --host 0.0.0.0 --port 5000

Screenshots

CLI

Laketower provides a CLI interface:

$ laketower --help

usage: laketower [-h] [--version] [--config CONFIG] {web,config,tables,queries} ...

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --config, -c CONFIG   Path to the Laketower YAML configuration file (default: laketower.yml)

commands:
  {web,config,tables,queries}
    web                 Launch the web application
    config              Work with configuration
    tables              Work with tables
    queries             Work with queries

By default, a YAML configuration file named laketower.yml will be looked for. A custom path can be specified with the -c / --config argument.

Validate YAML configuration

$ laketower -c demo/laketower.yml config validate

╭────────────────────────╮
│ Configuration is valid │
╰────────────────────────╯
Config(
    tables=[
        ConfigTable(name='sample_table', uri='demo/sample_table', table_format=<TableFormats.delta: 'delta'>),
        ConfigTable(name='weather', uri='demo/weather', table_format=<TableFormats.delta: 'delta'>)
    ]
)

List all registered tables

$ laketower -c demo/laketower.yml tables list

tables
├── sample_table
│   ├── format: delta
│   └── uri: demo/sample_table
└── weather
    ├── format: delta
    └── uri: demo/weather

Display a given table metadata

$ laketower -c demo/laketower.yml tables metadata sample_table

sample_table
├── name: Demo table
├── description: A sample demo Delta table
├── format: delta
├── uri: /Users/romain/Documents/dev/datalpia/laketower/demo/sample_table/
├── id: c1cb1cf0-1f3f-47b5-a660-3cc800edd341
├── version: 3
├── created at: 2025-02-05 22:27:39.579000+00:00
├── partitions:
└── configuration: {}

Display a given table schema

$ laketower -c demo/laketower.yml tables schema weather

weather
├── time: timestamp[us, tz=UTC]
├── city: string
├── temperature_2m: float
├── relative_humidity_2m: float
└── wind_speed_10m: float

Display a given table history

$ uv run laketower -c demo/laketower.yml tables history weather

weather
├── version: 2
│   ├── timestamp: 2025-02-05 22:27:46.425000+00:00
│   ├── client version: delta-rs.0.23.1
│   ├── operation: WRITE
│   ├── operation parameters
│   │   └── mode: Append
│   └── operation metrics
│       ├── execution_time_ms: 4
│       ├── num_added_files: 1
│       ├── num_added_rows: 168
│       ├── num_partitions: 0
│       └── num_removed_files: 0
├── version: 1
│   ├── timestamp: 2025-02-05 22:27:45.666000+00:00
│   ├── client version: delta-rs.0.23.1
│   ├── operation: WRITE
│   ├── operation parameters
│   │   └── mode: Append
│   └── operation metrics
│       ├── execution_time_ms: 4
│       ├── num_added_files: 1
│       ├── num_added_rows: 408
│       ├── num_partitions: 0
│       └── num_removed_files: 0
└── version: 0
    ├── timestamp: 2025-02-05 22:27:39.722000+00:00
    ├── client version: delta-rs.0.23.1
    ├── operation: CREATE TABLE
    ├── operation parameters
    │   ├── metadata: {"configuration":{},"createdTime":1738794459722,"description":"Historical and forecast weather data from
    │   │   open-meteo.com","format":{"options":{},"provider":"parquet"},"id":"a9615fb1-25cc-4546-a0fe-1cb534c514b2","name":"Weather","partitionCol
    │   │   umns":[],"schemaString":"{\"type\":\"struct\",\"fields\":[{\"name\":\"time\",\"type\":\"timestamp\",\"nullable\":true,\"metadata\":{}},
    │   │   {\"name\":\"city\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}},{\"name\":\"temperature_2m\",\"type\":\"float\",\"nullable\":
    │   │   true,\"metadata\":{}},{\"name\":\"relative_humidity_2m\",\"type\":\"float\",\"nullable\":true,\"metadata\":{}},{\"name\":\"wind_speed_1
    │   │   0m\",\"type\":\"float\",\"nullable\":true,\"metadata\":{}}]}"}
    │   ├── protocol: {"minReaderVersion":1,"minWriterVersion":2}
    │   ├── mode: ErrorIfExists
    │   └── location: file:///Users/romain/Documents/dev/datalpia/laketower/demo/weather
    └── operation metrics

Get statistics of a given table

Get basic statistics on all columns of a given table:

$ laketower -c demo/laketower.yml tables statistics weather

┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ column_name          ┃ count ┃ avg                ┃ std                ┃ min                    ┃ max                    ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
│ time                 │ 576   │ None               │ None               │ 2025-01-26 01:00:00+01 │ 2025-02-12 00:00:00+01 │
│ city                 │ 576   │ None               │ None               │ Grenoble               │ Grenoble               │
│ temperature_2m       │ 576   │ 5.2623263956047595 │ 3.326529069892729  │ 0.0                    │ 15.1                   │
│ relative_humidity_2m │ 576   │ 78.76909722222223  │ 15.701802163559918 │ 29.0                   │ 100.0                  │
│ wind_speed_10m       │ 576   │ 7.535763886032833  │ 10.00898058743763  │ 0.0                    │ 42.4                   │
└──────────────────────┴───────┴────────────────────┴────────────────────┴────────────────────────┴────────────────────────┘

Specifying a table version yields according results:

$ laketower -c demo/laketower.yml tables statistics --version 0 weather

┏━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━┳━━━━━━┳━━━━━━┳━━━━━━┓
┃ column_name          ┃ count ┃ avg  ┃ std  ┃ min  ┃ max  ┃
┡━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━╇━━━━━━╇━━━━━━╇━━━━━━┩
│ time                 │ 0     │ None │ None │ None │ None │
│ city                 │ 0     │ None │ None │ None │ None │
│ temperature_2m       │ 0     │ None │ None │ None │ None │
│ relative_humidity_2m │ 0     │ None │ None │ None │ None │
│ wind_speed_10m       │ 0     │ None │ None │ None │ None │
└──────────────────────┴───────┴──────┴──────┴──────┴──────┘

Import data into a given table

Import a CSV dataset into a table in append mode:

$ laketower -c demo/laketower.yml tables import weather --file data.csv --mode append --format csv --delimiter ',' --encoding 'utf-8'

--mode argument can be one of:

append: append rows to the table (default)
overwrite: replace all rows with the ones from the input file

--format argument can be one of:

csv: CSV file format (default)

--delimiter argument can be:

Any single character (only valid for CSV file format)
Default is comma (',')

--encoding argument can be:

Any standard Python encoding,
Default is 'utf-8'

View a given table

Using a simple query builder, the content of a table can be displayed. Optional arguments:

--cols <col1> <col2>: select which columns to display
--sort-asc <col>: sort by a column name in ascending order
--sort-desc <col>: sort by a column name in descending order
--limit <num> (default 10): limit the number of rows
--version: time-travel to table revision number

$ laketower -c demo/laketower.yml tables view weather

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ time                      ┃ city     ┃ temperature_2m     ┃ relative_humidity_2m ┃ wind_speed_10m    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ 2025-02-05 01:00:00+01:00 │ Grenoble │ 2.0                │ 84.0                 │ 4.0               │
│ 2025-02-05 02:00:00+01:00 │ Grenoble │ 2.0999999046325684 │ 83.0                 │ 1.5               │
│ 2025-02-05 03:00:00+01:00 │ Grenoble │ 1.600000023841858  │ 86.0                 │ 1.100000023841858 │
│ 2025-02-05 04:00:00+01:00 │ Grenoble │ 1.899999976158142  │ 80.0                 │ 4.199999809265137 │
│ 2025-02-05 05:00:00+01:00 │ Grenoble │ 1.899999976158142  │ 81.0                 │ 3.299999952316284 │
│ 2025-02-05 06:00:00+01:00 │ Grenoble │ 1.399999976158142  │ 88.0                 │ 4.300000190734863 │
│ 2025-02-05 07:00:00+01:00 │ Grenoble │ 1.7000000476837158 │ 87.0                 │ 5.5               │
│ 2025-02-05 08:00:00+01:00 │ Grenoble │ 1.5                │ 82.0                 │ 4.699999809265137 │
│ 2025-02-05 09:00:00+01:00 │ Grenoble │ 1.899999976158142  │ 80.0                 │ 2.200000047683716 │
│ 2025-02-05 10:00:00+01:00 │ Grenoble │ 2.9000000953674316 │ 80.0                 │ 0.800000011920929 │
└───────────────────────────┴──────────┴────────────────────┴──────────────────────┴───────────────────┘

$ laketower -c demo/laketower.yml tables view weather --cols time city temperature_2m --limit 5 --sort-desc time

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ time                      ┃ city     ┃ temperature_2m    ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ 2025-02-12 00:00:00+01:00 │ Grenoble │ 5.099999904632568 │
│ 2025-02-12 00:00:00+01:00 │ Grenoble │ 5.099999904632568 │
│ 2025-02-11 23:00:00+01:00 │ Grenoble │ 4.900000095367432 │
│ 2025-02-11 23:00:00+01:00 │ Grenoble │ 4.900000095367432 │
│ 2025-02-11 22:00:00+01:00 │ Grenoble │ 4.900000095367432 │
└───────────────────────────┴──────────┴───────────────────┘

$ laketower -c demo/laketower.yml tables view weather --version 1

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ time                      ┃ city     ┃ temperature_2m    ┃ relative_humidity_2m ┃ wind_speed_10m     ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 2025-01-26 01:00:00+01:00 │ Grenoble │ 7.0               │ 87.0                 │ 8.899999618530273  │
│ 2025-01-26 02:00:00+01:00 │ Grenoble │ 6.099999904632568 │ 87.0                 │ 6.199999809265137  │
│ 2025-01-26 03:00:00+01:00 │ Grenoble │ 6.0               │ 86.0                 │ 2.700000047683716  │
│ 2025-01-26 04:00:00+01:00 │ Grenoble │ 6.099999904632568 │ 82.0                 │ 3.0999999046325684 │
│ 2025-01-26 05:00:00+01:00 │ Grenoble │ 5.5               │ 87.0                 │ 3.299999952316284  │
│ 2025-01-26 06:00:00+01:00 │ Grenoble │ 5.199999809265137 │ 91.0                 │ 2.200000047683716  │
│ 2025-01-26 07:00:00+01:00 │ Grenoble │ 4.800000190734863 │ 86.0                 │ 3.0                │
│ 2025-01-26 08:00:00+01:00 │ Grenoble │ 4.900000095367432 │ 83.0                 │ 1.100000023841858  │
│ 2025-01-26 09:00:00+01:00 │ Grenoble │ 4.0               │ 92.0                 │ 3.0999999046325684 │
│ 2025-01-26 10:00:00+01:00 │ Grenoble │ 5.0               │ 86.0                 │ 6.400000095367432  │
└───────────────────────────┴──────────┴───────────────────┴──────────────────────┴────────────────────┘

Query all registered tables

Query any registered tables using DuckDB SQL dialect!

$ laketower -c demo/laketower.yml tables query "select date_trunc('day', time) as day, avg(temperature_2m) as mean_temperature from weather group by day order by day desc limit 3"

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ day                       ┃ mean_temperature   ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 2025-02-12 00:00:00+01:00 │ 5.099999904632568  │
│ 2025-02-11 00:00:00+01:00 │ 4.833333373069763  │
│ 2025-02-10 00:00:00+01:00 │ 2.1083333243926368 │
└───────────────────────────┴────────────────────┘
3 rows returned
Execution time: 33.72ms

Use named parameters within a giving query (note: escape $ prefixes properly!):

$ laketower -c demo/laketower.yml tables query "select date_trunc('day', time) as day, avg(temperature_2m) as mean_temperature from weather where day between \$start_date and \$end_date group by day order by day desc" -p start_date 2025-01-29 -p end_date 2025-01-31

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ day                       ┃ mean_temperature   ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ 2025-01-31 00:00:00+01:00 │ 5.683333257834117  │
│ 2025-01-30 00:00:00+01:00 │ 8.900000015894571  │
│ 2025-01-29 00:00:00+01:00 │ 7.770833313465118  │
└───────────────────────────┴────────────────────┘
4 rows returned
Execution time: 30.59ms

Export query results to CSV:

$ laketower -c demo/laketower.yml tables query --output results.csv "select date_trunc('day', time) as day, avg(temperature_2m) as mean_temperature from weather group by day order by day desc limit 3"

Query results written to: results.csv

List saved queries

$ laketower -c demo/laketower.yml queries list

queries
├── all_data
└── daily_avg_temperature

Execute saved queries

$ laketower -c demo/laketower.yml queries view daily_avg_temperature

┏━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ #     ┃ day                       ┃ avg_temperature ┃
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ 1     │ 2025-01-26 00:00:00+01:00 │ 8.0             │
│ 2     │ 2025-01-27 00:00:00+01:00 │ 13.0            │
│ 3     │ 2025-01-28 00:00:00+01:00 │ 7.0             │
│ 4     │ 2025-01-29 00:00:00+01:00 │ 8.0             │
│ 5     │ 2025-01-30 00:00:00+01:00 │ 9.0             │
│ 6     │ 2025-01-31 00:00:00+01:00 │ 6.0             │
│ 7     │ 2025-02-01 00:00:00+01:00 │ 4.0             │
│ 8     │ 2025-02-02 00:00:00+01:00 │ 4.0             │
│ 9     │ 2025-02-03 00:00:00+01:00 │ 4.0             │
│ 10    │ 2025-02-04 00:00:00+01:00 │ 3.0             │
│ 11    │ 2025-02-05 00:00:00+01:00 │ 3.0             │
│ 12    │ 2025-02-06 00:00:00+01:00 │ 2.0             │
│ 13    │ 2025-02-07 00:00:00+01:00 │ 6.0             │
│ 14    │ 2025-02-08 00:00:00+01:00 │ 7.0             │
│ 15    │ 2025-02-09 00:00:00+01:00 │ 5.0             │
│ 16    │ 2025-02-10 00:00:00+01:00 │ 2.0             │
│ 17    │ 2025-02-11 00:00:00+01:00 │ 5.0             │
│ 18    │ 2025-02-12 00:00:00+01:00 │ 5.0             │
├───────┼───────────────────────────┼─────────────────┤
│ Total │ -                         │ 101.0           │
└───────┴───────────────────────────┴─────────────────┘
18 rows returned
Execution time: 27.02ms

Executing a predefined query with parameters (here start_date and end_date):

$ laketower -c demo/laketower.yml queries view daily_avg_temperature_params -p start_date 2025-02-01 -p end_date 2025-02-05

┏━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ # ┃ day                       ┃ avg_temperature ┃
┡━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ 1 │ 2025-02-01 00:00:00+01:00 │ 4.0             │
│ 2 │ 2025-02-02 00:00:00+01:00 │ 4.0             │
│ 3 │ 2025-02-03 00:00:00+01:00 │ 4.0             │
│ 4 │ 2025-02-04 00:00:00+01:00 │ 3.0             │
│ 5 │ 2025-02-05 00:00:00+01:00 │ 3.0             │
│ 6 │ 2025-02-06 00:00:00+01:00 │ 2.0             │
└───┴───────────────────────────┴─────────────────┘
6 rows returned
Execution time: 29.70ms

License

Licensed under Apache License 2.0

Name		Name	Last commit message	Last commit date
Latest commit History 271 Commits
.github/workflows		.github/workflows
demo		demo
docs/static		docs/static
laketower		laketower
tests		tests
.gitignore		.gitignore
.python-version		.python-version
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
pyproject.toml		pyproject.toml
renovate.json		renovate.json
tasks.py		tasks.py
uv.lock		uv.lock

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