Sunday, November 30, 2025

What is Databricks?

Databricks is a unified data analytics platform built by the creators of Apache Spark. It provides a collaborative cloud-based environment for:

Key Capabilities:

Data Engineering: ETL, data processing, and pipeline management

Data Science & ML: End-to-end machine learning lifecycle

Data Analytics: SQL analytics, business intelligence, and reporting

Data Warehousing: Delta Lake for reliable data lakes

Collaboration: Shared workspaces, notebooks, and dashboards

Core Components:

Databricks Workspace: Collaborative environment with notebooks, dashboards

Databricks Runtime: Optimized Apache Spark environment

Delta Lake: ACID transactions for data lakes

MLflow Integration: Native machine learning lifecycle management

Unity Catalog: Unified governance for data and AI


How Databricks Relates to MLflow

1. MLflow was Created by Databricks

MLflow was originally developed at Databricks as an open-source project


It's now a popular standalone open-source platform for managing the ML lifecycle


2. Native Integration

Databricks provides deep, native integration with MLflow:


# MLflow is automatically available in Databricks notebooks

import mlflow


# Automatic tracking in Databricks

with mlflow.start_run():

    mlflow.log_param("learning_rate", 0.01)

    mlflow.log_metric("accuracy", 0.95)

    mlflow.sklearn.log_model(model, "model")


3. MLflow Tracking Server Built-in

Automatic experiment tracking in Databricks workspace


Centralized model registry for model versioning and staging


UI integration - MLflow experiments visible directly in Databricks UI


4. Enhanced Features in Databricks

Automated MLflow logging for popular libraries (scikit-learn, TensorFlow, etc.)


Managed MLflow - No setup required, fully managed service


Unity Catalog integration - Model lineage and governance


Feature Store integration - Managed feature platform


5. End-to-End ML Platform

Databricks + MLflow provides:


Data Preparation → Model Training → Experiment Tracking → 

Model Registry → Deployment → Monitoring

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