DLT-META

Documentation | Release Notes | Examples

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Project Overview

DLT-META is a metadata-driven framework designed to work with Delta Live Tables. This framework enables the automation of bronze and silver data pipelines by leveraging metadata recorded in an onboarding JSON file. This file, known as the Dataflowspec, serves as the data flow specification, detailing the source and target metadata required for the pipelines.

In practice, a single generic DLT pipeline reads the Dataflowspec and uses it to orchestrate and run the necessary data processing workloads. This approach streamlines the development and management of data pipelines, allowing for a more efficient and scalable data processing workflow

Components:

Metadata Interface

• Capture input/output metadata in onboarding file
• Capture Data Quality Rules
• Capture processing logic as sql in Silver transformation file

Generic DLT pipeline

• Apply appropriate readers based on input metadata
• Apply data quality rules with DLT expectations
• Apply CDC apply changes if specified in metadata
• Builds DLT graph based on input/output metadata
• Launch DLT pipeline

High-Level Process Flow:

Steps

Getting Started

Refer to the Getting Started

Databricks Labs DLT-META CLI lets you run onboard and deploy in interactive python terminal

pre-requisites:

• Python 3.8.0 +

• Databricks CLI v0.213 or later. See instructions

• Install Databricks CLI on macOS:

• 

• Install Databricks CLI on Windows:

• 

Once you install Databricks CLI, authenticate your current machine to a Databricks Workspace:

databricks auth login --host WORKSPACE_HOST

To enable debug logs, simply add `--debug` flag to any command.

Installing dlt-meta:

• Install dlt-meta via Databricks CLI:

    databricks labs install dlt-meta

Onboard using dlt-meta CLI:

If you want to run existing demo files please follow these steps before running onboard command:

    git clone https://github.com/databrickslabs/dlt-meta.git

    cd dlt-meta

    python -m venv .venv

    source .venv/bin/activate

    pip install databricks-sdk

    dlt_meta_home=$(pwd)

    export PYTHONPATH=$dlt_meta_home

    databricks labs dlt-meta onboard

Above commands will prompt you to provide onboarding details. If you have cloned dlt-meta git repo then accept defaults which will launch config from demo folder.

• Goto your databricks workspace and located onboarding job under: Workflow->Jobs runs

depoly using dlt-meta CLI:

• Once onboarding jobs is finished deploy bronze and silver DLT using below command

•    databricks labs dlt-meta deploy
  

• • Above command will prompt you to provide dlt details. Please provide respective details for schema which you provided in above steps
• • Bronze DLT

• Silver DLT

• •    databricks labs dlt-meta deploy
    

• • Above command will prompt you to provide dlt details. Please provide respective details for schema which you provided in above steps

More questions

Refer to the FAQ and DLT-META documentation

Project Support

Please note that all projects released under Databricks Labs are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.

Any issues discovered through the use of this project should be filed as issues on the Github Repo.
They will be reviewed as time permits, but there are no formal SLAs for support.