Introduction to Azure Databricks

Azure tutorial · PySpark.in

1. What is Azure Databricks?

At the core of Databricks is Apache Spark, an open-source distributed compute engine.

Apache Spark is widely used for big data and machine learning projects.
Databricks was founded by the creators of Apache Spark to make Spark easier to use by adding essential management layers.
It is available on all major cloud platforms: Microsoft Azure, AWS, and Google Cloud.

On Azure, Databricks is offered as a first-party service, meaning Microsoft provides unified billing, native support, and tight integration with other Azure services.

2. Apache Spark: The Foundation

Before we dive deeper into Databricks, let’s understand Spark.

Developed at UC Berkeley (2009) → Open-sourced in 2010 → Became an Apache project in 2013.
Adopted by companies like Yahoo, eBay, Netflix to handle petabytes of data.
Designed to overcome the limitations of Hadoop (which was slower and inefficient for iterative/interactive tasks).

Key Features of Spark

Up to 100x faster than Hadoop using in-memory computing.
Unified engine for batch and streaming workloads.
Built-in libraries for SQL, machine learning (MLlib), and graph processing (GraphX).

3. Why Databricks?

While Spark is powerful, managing it can be challenging:

Setting up clusters
Managing security
Integrating with external tools

Databricks solves these problems by providing:

One-click cluster setup (general purpose, memory-optimized, GPU support).
Integrated Notebook IDE for coding, visualization, and collaboration.
Admin controls for security and user management.
Optimized Spark Runtime (up to 5x faster than open-source Spark).
Photon Engine → Vectorized query engine, up to 8x faster.

4. Key Features of Azure Databricks

Data Management

Hive Metastore or Unity Catalog for managing databases & tables.
Unity Catalog adds data lineage & governance.
Delta Lake for ACID transactions, ensuring data reliability.
Delta Live Tables → Declarative ETL framework for easy pipelines.

Data Engineering

Databricks Workflows → Orchestrate tasks & pipelines.
Databricks SQL / SQL Warehouse → For analysts to query, explore, and build dashboards.

Machine Learning

Managed MLflow → Supports experimentation, deployment, and model registry.

AI Assistance

Databricks IQ → Helps debug code, generate queries, and build dashboards.

5. Azure Integration

Databricks integrates deeply with Azure services:

Security & Identity: Microsoft Entra ID, Single Sign-On.
Data Storage: Azure Data Lake Gen2, Cosmos DB, Azure SQL, Synapse.
Streaming: IoT Hub, Event Hub.
Monitoring: Azure Monitor for workload insights.
DevOps: Azure DevOps for CI/CD integration.

The clusters run on Azure VMs, and all services are billed as part of your Azure subscription.