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INDIA'S NO. 1 INTERNSHIP PORTAL
🎉 Festival Dhamaka Sale – Upto 80% Off on All Courses 🎊
🎁INDIA'S NO. 1 INTERNSHIP PORTAL
Combines data lakes and warehouses for unified storage, governance, and analytics.
Supports Python, SQL, Scala, and R with real-time co-authoring and visualizations.
Provision Spark clusters on demand with optimized resource management.
Train, track, and deploy models with MLflow, Feature Store, and Model Registry.
Sign up via AWS, Azure, or GCP and launch your workspace from the cloud console.
Use Delta Lake, cloud storage, or JDBC connectors to ingest structured and unstructured data.
Write code in Python, SQL, or Scala to transform, analyze, and visualize data.
Use MLflow to log experiments, tune hyperparameters, and register models.
Serve models via REST endpoints and monitor performance with built-in dashboards.
# 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))Build scalable pipelines with Spark SQL, Delta Lake, and workflow orchestration.
Train, tune, and deploy models with MLflow and collaborative notebooks.
Stream data using Structured Streaming and visualize with dashboards.
Unify batch and streaming workloads with governance and performance.
Explore Databricks’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about Databricks’s capabilities, usage, and ecosystem.