🗼 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

storage_credentials:
  <credential_name>:
    s3:               # mutually exclusive with adls
      access_key_id: <access-key-id>
      secret_access_key: <secret-access-key>
      region: <region>
      endpoint_url: <endpoint-url>
      allow_http: false
    adls:             # mutually exclusive with s3
      account_name: <account-name>
      access_key: <access-key>
      sas_key: <sas-key>
      tenant_id: <tenant-id>
      client_id: <client-id>
      client_secret: <client-secret>
      msi_endpoint: <msi-endpoint>
      use_azure_cli: false

tables:
  - name: <table_name>
    uri: <local or remote path to table>
    format: {delta}
    storage_credential: <credential_name>   # optional, references storage_credentials

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

For string values that only need partial substitution, use the ${VAR_NAME} inline syntax instead. Multiple variables in a single value are supported:

# export BUCKET=my-bucket
# export PREFIX=my-prefix

tables:
  - name: sample_table
    uri: s3://${BUCKET}/${PREFIX}/sample_table
    format: delta

The two syntaxes are independent: {env: VAR} replaces the entire value (any type), while ${VAR} interpolates within a string.

Config Includes

Large or multi-environment setups can split the configuration across multiple YAML files using the include directive. Included files are deep-merged before the main file, so the main file always wins on conflict.

# queries.yml  (shared across environments)
queries:
  - name: all_data
    title: All data
    sql: "select * from my_table"

# laketower.production.yml
include:
  - queries.yml

storage_credentials:
  s3_creds:
    s3:
      access_key_id: ${AWS_ACCESS_KEY_ID}
      secret_access_key: ${AWS_SECRET_ACCESS_KEY}

tables:
  - name: my_table
    uri: s3://production-bucket/tables/my_table
    format: delta
    storage_credential: s3_creds

Merge semantics:

• Lists (tables, queries, ...): included file items come first, main file items appended
• Dicts (settings, storage_credentials, ...): recursively deep-merged, main file values win on conflict
• Scalars: main file wins

Rules:

• include paths are relative to the directory of the file declaring them
• Multiple files are merged in order (first listed = lowest priority)
• Environment variable substitution works in included files
• Included files themselves do not support include (no recursive includes)

Storage Credentials

Storage credentials are defined once under the top-level storage_credentials key as a named registry, then referenced by name from each table via the storage_credential field. This avoids repeating the same credentials across multiple tables.

Remote S3 Tables

Configuring S3 tables (AWS, MinIO, Cloudflare R2, Scaleway Object Storage, …):

storage_credentials:
  my_s3:
    s3:
      access_key_id: access-key-id
      secret_access_key: secret-access-key
      region: s3-region
      endpoint_url: http://s3.domain.com
      allow_http: false

tables:
  - name: delta_table_s3
    uri: s3://<bucket>/path/to/table
    format: delta
    storage_credential: my_s3

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

As a security best practice, avoid writing secrets directly in static configuration files. Use environment variable substitution instead:

storage_credentials:
  my_s3:
    s3:
      access_key_id: access-key-id
      secret_access_key:
        env: S3_SECRET_ACCESS_KEY
      region: s3-region
      endpoint_url: http://s3.domain.com
      allow_http: false

tables:
  - name: delta_table_s3
    uri: s3://<bucket>/path/to/table
    format: delta
    storage_credential: my_s3

Remote ADLS Tables

Configuring Azure ADLS tables:

storage_credentials:
  my_adls:
    adls:
      account_name: adls-account-name
      access_key: adls-access-key
      sas_key: adls-sas-key
      tenant_id: adls-tenant-id
      client_id: adls-client-id
      client_secret: adls-client-secret
      msi_endpoint: https://msi.azure.com
      use_azure_cli: false

tables:
  - name: delta_table_adls
    uri: abfss://<container>/path/to/table
    format: delta
    storage_credential: my_adls

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

As a security best practice, avoid writing secrets directly in static configuration files. Use environment variable substitution instead:

storage_credentials:
  my_adls:
    adls:
      account_name: adls-account-name
      access_key:
        env: ADLS_ACCESS_KEY

tables:
  - name: delta_table_adls
    uri: abfss://<container>/path/to/table
    format: delta
    storage_credential: my_adls

Predefined Query Parameters

Predefined queries allows for specifying named parameters that can then be used with an SQL statement using the $param_name syntax.

When a query parameter is left blank by the user, it is treated as NULL by the SQL engine. Use COALESCE to provide a fallback so the filter becomes a no-op instead of returning an error or empty results:

queries:
  - name: daily_avg_temperature_params
    title: Daily average temperature with parameters
    parameters:
      start_date:
        default: "2025-01-01"
      end_date:
        default: "2025-01-31"
    sql: |
      select
        date_trunc('day', time) as day,
        round(avg(temperature_2m)) as avg_temperature
      from
        weather
      where
        day between coalesce($start_date::timestamp, timestamp '-infinity')
                and coalesce($end_date::timestamp, timestamp 'infinity')
      group by
        day
      order by
        day asc

In this example:

• Blank start_date leads to timestamp '-infinity' (no lower bound)
• Blank end_date leads to timestamp 'infinity' (no upper bound)
• If both parameters are blank, all rows are returned

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.

Show resolved YAML configuration

Print the fully resolved configuration after include merging, useful for debugging composed setups.

$ laketower -c demo/laketower.yml config show

Pass --with-env-vars-substitution to also resolve environment variable references:

$ laketower -c demo/laketower.yml config show --with-env-vars-substitution

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)
• xlsx: Excel file format (requires laketower[excel], imports the first sheet)

--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'
• Only applies to CSV file format

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

Copyright (c) 2025 - present Romain Clement / Datalpia