Key Features
Lakehouse Architecture
Combines data lakes and warehouses for unified storage, governance, and analytics.
Collaborative Notebooks
Supports Python, SQL, Scala, and R with real-time co-authoring and visualizations.
Auto-Scaling Clusters
Provision Spark clusters on demand with optimized resource management.
ML & MLOps Integration
Train, track, and deploy models with MLflow, Feature Store, and Model Registry.
How It Works
Create Databricks Workspace
Sign up via AWS, Azure, or GCP and launch your workspace from the cloud console.
Upload or Connect Data
Use Delta Lake, cloud storage, or JDBC connectors to ingest structured and unstructured data.
Build Notebooks
Write code in Python, SQL, or Scala to transform, analyze, and visualize data.
Train & Track Models
Use MLflow to log experiments, tune hyperparameters, and register models.
Deploy & Monitor
Serve models via REST endpoints and monitor performance with built-in dashboards.
Code Example
# PySpark + MLflow example in Databricks
import mlflow
import pandas as pd
from sklearn.linear_model import LinearRegression
# Load data
df = pd.read_csv("/dbfs/data/sales.csv")
X = df[["ad_spend", "email_clicks"]]
y = df["revenue"]
# Train model
model = LinearRegression()
model.fit(X, y)
# Log with MLflow
mlflow.sklearn.log_model(model, "linear-model")
mlflow.log_metric("r2", model.score(X, y))Use Cases
ETL & Data Engineering
Build scalable pipelines with Spark SQL, Delta Lake, and workflow orchestration.
Machine Learning Lifecycle
Train, tune, and deploy models with MLflow and collaborative notebooks.
Real-Time Analytics
Stream data using Structured Streaming and visualize with dashboards.
Enterprise Data Lakehouse
Unify batch and streaming workloads with governance and performance.
Integrations & Resources
Explore Databricks’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- Apache Spark & Delta Lake
- MLflow & scikit-learn
- AWS S3, Azure Blob, GCP Storage
- Power BI, Tableau, Looker
- Airflow, dbt, Kubernetes
Helpful Resources
FAQ
Common questions about Databricks’s capabilities, usage, and ecosystem.