Azure Databricks tutorial roadmap (Beginner → Advanced), tailored for Data Engineering interviews in India

Here’s a complete Azure Databricks tutorial roadmap (Beginner → Advanced), tailored for Data Engineering interviews in India, including key concepts, technical terms, use cases, and interview Q&A:

✅ What is Azure Databricks?

Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for the Microsoft Azure cloud.

Built by the creators of Apache Spark.
Combines big data and AI workloads.
Supports data engineering, machine learning, streaming, and analytics.

🔗 How Azure Databricks integrates with Azure (vs AWS Databricks)

Feature	Azure Databricks	AWS Databricks
Native Integration	Deep integration with Azure services (e.g., Azure Data Lake, Azure Synapse, Key Vault, Blob)	Native to AWS services (e.g., S3, Glue, Redshift)
Identity & Security	Azure Active Directory (AAD) for login + RBAC	IAM-based permissions
Networking	VNet Injection, Private Link	VPC Peering, Transit Gateway
Resource Management	Managed via Azure Portal, ARM templates	Managed via AWS Console, CloudFormation
Cluster Management	Azure-managed, integrated billing	AWS-managed

🧠 Databricks Workspace Components

1. 🔢 Notebooks

Interactive interface to run code, visualize data, and write Markdown.
Supports multiple languages: PySpark, SQL, Scala, R, Python.
Versioning and collaboration built-in.

2. 📁 Repos

Git integration for source control.
Supports GitHub, Azure DevOps, Bitbucket, GitLab.
Enables CI/CD workflows for Notebooks and Jobs.

3. ⚙️ Clusters

Compute resources to run code.
Types:
- Interactive Clusters (for exploration)
- Job Clusters (for scheduled jobs)
Auto-scaling, auto-termination, and Spark runtime configurations.

🏠 Lakehouse Architecture (Delta Lake + Spark)

Lakehouse = Data Warehouse reliability + Data Lake flexibility

🔶 Delta Lake

Open-source storage layer built on top of Parquet.
Adds:
- ACID Transactions
- Schema Enforcement
- Time Travel
- Efficient Upserts (MERGE INTO)

🔁 Layers in Lakehouse

Layer	Purpose	Example
Bronze	Raw ingestion from source systems	Raw JSON/CSV from Kafka, logs
Silver	Cleaned, joined data	Filtered customer data
Gold	Business-ready aggregations	Monthly revenue reports

🚀 Apache Spark Basics

Apache Spark is a unified big data engine for:

Batch + Streaming
SQL + ML + Graph
In-memory processing for speed

🔹 RDD (Resilient Distributed Dataset)

Low-level immutable distributed collection.
Good for custom transformations, fine-grained control.

rdd = spark.sparkContext.parallelize([1, 2, 3])
result = rdd.map(lambda x: x * x).collect()

✅ Use Cases:

Log parsing
Custom ETL logic
Fault-tolerant distributed computing

🔸 DataFrame

High-level, distributed table-like structure.
Optimized via Catalyst Optimizer + Tungsten Engine.

df = spark.read.csv("sales.csv", header=True, inferSchema=True)
df.filter("amount > 500").groupBy("region").sum("amount").show()