Azure Databricks is an analytics platform optimized for big data and machine learning workloads. The most appropriate and recommended storage solution for persisting raw ingested data for these workloads is Azure Data Lake Storage (ADLS) Gen2. ADLS Gen2 is specifically designed for big data analytics, providing a highly scalable, cost-effective data lake with a hierarchical namespace. This structure is crucial for organizing and efficiently processing massive volumes of structured, semi-structured, and unstructured data using the parallel processing capabilities of Apache Spark within Azure Databricks.

A. Azure Files: This is a managed file share service (SMB protocol) intended for lift-and-shift applications, not for the high-throughput, parallel I/O required by big data analytics platforms.

C. Azure SQL Database: This is a relational database (OLTP) optimized for transactional workloads with structured data, not for storing and processing large-scale, diverse raw data sets.

D. Azure Cosmos DB: This is a NoSQL database designed for low-latency, high-availability operational applications, making it less cost-effective and suitable for bulk data storage and batch analytics.

1. Microsoft Learn

Azure Databricks Documentation

"What is a data lakehouse?": This document explicitly states

"The data lakehouse architecture is enabled by a new

open

and standardized system design: storing data in open-source file formats (like Parquet) in a data lake (like Azure Data Lake Storage Gen2)

and having a transactional metadata layer on top (like Delta Lake)." This positions ADLS Gen2 as the foundational storage layer.

2. Microsoft Learn

Azure Databricks Documentation

"Tutorial: Azure Data Lake Storage Gen2

Azure Databricks & Spark": The introduction of this tutorial states

"This tutorial shows you how to connect your Azure Databricks cluster to an Azure storage account with Azure Data Lake Storage Gen2 enabled." This demonstrates the primary and intended integration pattern.

3. Microsoft Learn

Azure Architecture Center

"Modern analytics architecture with Azure Databricks": In the "Data flow" section

step 1 describes data ingestion: "Raw structured

semi-structured

and unstructured (text

image

audio

and video) data is ingested... and stored in Azure Data Lake Storage Gen2." This reference confirms ADLS Gen2 as the designated persistence layer for ingested data in a standard Databricks architecture.