laktory 0.0.7

A DataOps framework for building a lakehouse

Laktory

A DataOps framework for building Databricks lakehouse.

Okube Company

Okube is committed to develop open source data and ML engineering tools. This is an open space. Contributions are more than welcome.

Help

TODO: Build full help documentation

Installation

Install using pip install laktory

TODO: Full installation instructions

A Basic Example

This example demonstrates how to send data events to a data lake and to set a data pipeline defining the tables transformation layers.

Generate data events

A data event class defines specifications of an event and provides the methods for writing that event to a databricks mount or a cloud storage.

from laktory import models
from datetime import datetime


events = [
    models.DataEvent(
        name="stock_price",
        producer={
            "name": "yahoo-finance",
        },
        data={
            "created_at": datetime(2023, 8, 23),
            "symbol": "GOOGL",
            "open": 130.25,
            "close": 132.33,
        },
    ),
    models.DataEvent(
        name="stock_price",
        producer={
            "name": "yahoo-finance",
        },
        data={
            "created_at": datetime(2023, 8, 24),
            "symbol": "GOOGL",
            "open": 132.00,
            "close": 134.12,
        },
    )
]

for event in events:
    event.to_databricks_mount()

These events may now be sent to your cloud storage of choice.

Define data pipeline and data tables

A pipeline class defines the transformations of a raw data event into curated (silver) and consumption (gold) layers.

from laktory import models

pl = models.Pipeline(
    name="pl-stock-prices",
    tables=[
        models.Table(
            name="brz_stock_prices",
            timestamp_key="data.created_at",
            event_source=models.EventDataSource(
                name="stock_price",
                producer=models.Producer(
                    name="yahoo-finance",
                )
            ),
            zone="BRONZE",
        ),
        models.Table(
            name="brz_stock_prices",
            table_source=models.TableSource(
                name="brz_stock_prices",
            ),
            zone="SILVER",
            columns = [
                {
                    "name": "created_at",
                    "type": "timestamp",
                    "func_name": "coalesce",
                    "input_cols": ["_created_at"],
                },
                {
                    "name": "low",
                    "type": "double",
                    "func_name": "coalesce",
                    "input_cols": ["data.low"],
                },
                {
                    "name": "high",
                    "type": "double",
                    "func_name": "coalesce",
                    "input_cols": ["data.high"],
                },
            ]
        ),
    ]
)

Laktory will provide the required framework for deploying this pipeline as a delta live tables in Databricks and all the associated notebooks and jobs. TODO: link to help

Contributing

TODO