Databricks Architecture Explained

Introduction

Databricks architecture is designed to support scalable analytics and distributed data processing using Apache Spark.

Step 1: Control Plane

The control plane manages the workspace UI, notebooks, jobs, and cluster management.

Step 2: Data Plane

The data plane contains the compute clusters where Spark jobs are executed.

Step 3: Storage Layer

Databricks stores data in cloud storage such as AWS S3, Azure Data Lake, or Google Cloud Storage.

Conclusion

The separation between control plane and data plane allows Databricks to provide high scalability and security.

AWS S3 Explained: Buckets, Storage Classes, Security & Use Cases

AWS S3 Explained — Buckets, Storage Classes, Security & Use Cases

What Is Amazon S3?

Amazon S3 (Simple Storage Service) is an object storage service that provides 11 nines durability (99.999999999%). It stores data as objects inside buckets.

Core S3 Concepts

• Buckets: Top-level container
• Objects: Files stored inside buckets
• Keys: Object names
• Versioning: Tracks old versions of objects
• Encryption: SSE-S3, SSE-KMS

Storage Classes

• S3 Standard
• S3 Infrequent Access (IA)
• S3 One Zone IA
• S3 Glacier
• S3 Glacier Deep Archive

Useful S3 Features

• Bucket policies
• Lifecycle rules
• Cross-Region Replication
• S3 Events (trigger Lambda)
• Access Control Lists

Use Cases

• Static website hosting
• Backups and archives
• Data lakes
• Log storage
• Machine learning datasets

Conclusion

S3 is the most flexible cloud storage solution. It is widely used in multiple industries and AWS exams.