Laktory Documentation

Laktory is built on top of a collection of nested pydantic models. Each model is a subclass of pydantic.BaseModel and offers a few additional methods and properties. The serializable nature of these models makes it possible to define a lakehouse using a declarative approach.

Types

Models generally fall into two categories:

• Deployable: Resources defined with the intent of deploying them to a cloud or data platform provider. These resources are found under laktory.models.resources.
• Executable: Resources defined with the intent of executing them on a local workstation or a remote compute resource. A notable example is the laktory.models.pipeline model.

Declaration

Because all models are serializable, they can be declared either directly in Python code or as external YAML or JSON files. The latter is often preferable in a DataOps approach, where the focus is on configuring models rather than writing boilerplate code.

Below is an example of how Catalog, Schema, and Table models can be declared using both approaches.

YAMLPython

catalog.yaml

name: prod
comment: Production catalog
schemas: 
  - name: finance
    tables:
      - name: bronze
      - name: silver
      - name: gold
  - name: engineering
    tables:
      - name: bronze
      - name: silver
      - name: gold

main.py

from laktory import models

with open("catalog.yaml", "r") as fp:
    schema = models.resources.databricks.Catalog.model_validate_yaml(fp)

from laktory.models.resources.databricks import Catalog
from laktory.models.resources.databricks import Schema
from laktory.models.resources.databricks import Table

bronze = Table(name="bronze")
silver = Table(name="bronze")
gold = Table(name="gold")

catalog = Catalog(
    name="prod",
    comment="Production catalog",
    schemas=[
        Schema(name="finance", tables=[bronze, silver, gold]),
        Schema(name="engineering", tables=[bronze, silver, gold]),
    ]
)

Both approaches result in the exact same catalog Python object.

YAML nesting

Laktory supports nested YAML files, allowing you to reference another YAML file within a YAML file using custom tags.

Direct injection

With the !use tag, you can inject the content of a YAML file directly where it is referenced. The example above can be rewritten as:

catalog.yaml

name: "production"
comment: Production catalog
schemas: !use schemas.yaml

schemas.yaml

- name: finance
  tables: !use tables.yaml
- name: engineering
  tables: !use tables.yaml

tables.yaml

- name: bronze
- name: silver
- name: gold

List Concatenation

In addition to direct injection, Laktory also supports the !extend tag to concatenate two lists. For example:

Using this tag, the model

catalog.yaml

name: production
schemas:
- name: sandbox
- name: bronze
- name: silver
- name: gold

could be re-written as:

catalog.yaml

name: production
schemas:
- name: sandbox
- !extend common_schemas.yaml 

common_schemas.yaml

- name: bronze
- name: silver
- name: gold

This is convenient for sharing components across multiple objects.

Dictionary merge

The !update tag allows merging the content of two dictionaries. For instance:

catalog.yaml

name: production
<<: !update catalog_properties.yaml

catalog_properties.yaml

isolation_mode: OPEN
owner: laktory

Is equivalent to:

catalog.yaml

name: production
isolation_mode: OPEN
owner: laktory

Variables

Laktory models support variables to facilitate parameterization or to reference values unavailable at declaration time.

catalog.yaml

name: ${vars.env}
comment: Production catalog
schemas: 
  - name: ${vars.env}-finance
  - name: ${vars.env}-engineering
variables:
    env: prod

For more information, refer to the variables documentation.

Stack

The laktory.models.Stack model acts as a container for declaring a collection of cloud-deployable resources. It serves as the main entry point for the Laktory CLI. For more information, refer to the stack documentation.