laktory 0.1.6

A DataOps framework for building a lakehouse

Laktory

A DataOps framework for building Databricks lakehouse.

Laktory makes it possible to express and bring to life your data vision, from raw to enriched analytics-ready datasets and finely tuned AI models, while adhering to basic DevOps best practices such as source control, code reviews and CI/CD.

Using a declarative approach, you define your datasets and transformations, validate them and deploy them into Databricks workspaces. Once deployed, you can once again leverage Laktory for debugging and monitoring.

Help

See documentation for more details.

Installation

Install using

pip install laktory[{cloud_provider}]

where {cloud_provider} is azure, aws or gcp.

For more installation options, see the Install section in the documentation.

A Basic Example

from laktory import models

table = models.Table(
    name="brz_stock_prices",
    catalog_name="prod",
    schema_name="finance",
    timestamp_key="data.created_at",
    builder={
        "layer": "SILVER",
        "table_source": {
            "name": "brz_stock_prices",
        }
    },
    columns=[
        {
            "name": "symbol",
            "type": "string",
            "sql_expression": "data.symbol"
        }
    ]
)

print(table)
#> catalog_name='prod' columns=[Column(catalog_name='prod', comment=None, name='symbol', pii=None, schema_name='finance', spark_func_args=[], spark_func_kwargs={}, spark_func_name=None, sql_expression='data.symbol', table_name='brz_stock_prices', type='string', unit=None)] comment=None data=None grants=None name='brz_stock_prices' primary_key=None schema_name='finance' timestamp_key='data.created_at' builder=TableBuilder(drop_source_columns=True, drop_duplicates=None, event_source=None, joins=[], pipeline_name=None, table_source=TableDataSource(read_as_stream=True, catalog_name='prod', cdc=None, selects=None, filter=None, from_pipeline=True, name='brz_stock_prices', schema_name='finance', watermark=None), layer='SILVER')

To get started with a more useful example, jump into the Quickstart.

A full Data Ops template

A comprehensive template on how to deploy a lakehouse as code using Laktory is maintained here: https://github.com/okube-ai/lakehouse-as-code.

In this template, 4 pulumi projects are used to:

• {cloud_provider}_infra: Deploy the required resources on your cloud provider
• unity-catalog: Setup users, groups, catalogs, schemas and manage grants
• workspace-conf: Setup secrets, clusters and warehouses
• workspace: The data workflows to build your lakehouse.

Okube Company

Okube is dedicated to building open source frameworks, known as the kubes, empowering businesses to build, deploy and operate highly scalable data platforms and AI models.