Both Azure SQL and ADLS Gen2 can be integrated with Databricks, but the use case and best practices differ.
### ๐ **Connecting Databricks to ADLS Gen2**
**Best for:** Storing large volumes of raw/semi-structured/structured data (data lake use cases).
**Approach:**
* Use **Azure Data Lake Storage Gen2** with **ABFS (Azure Blob File System)** or **OAuth (Service Principal or Managed Identity)** for secure access.
* Mount the ADLS Gen2 path using `dbutils.fs.mount()` (if needed) or access it directly with ABFS path.
**Pros:**
* Scalable and cost-effective for large data volumes.
* Native support in Databricks for Delta Lake (ACID).
* Supports Medallion Architecture (Bronze, Silver, Gold).
* Ideal for big data and ML pipelines.
**Cons:**
* Not ideal for transactional updates (OLTP-style).
* Slightly more setup required for secure integration (Key Vault, Service Principal, etc.).
---
### ๐ **Connecting Databricks to Azure SQL Database**
**Best for:** Reading/writing smaller volumes of structured data or integrating with existing RDBMS systems.
**Approach:**
* Use the **JDBC driver** to read/write from Azure SQL.
* Use **Azure Key Vault** to securely store credentials.
* Consider **incremental loads** with change tracking or timestamps.
**Pros:**
* Good for point lookups, dimension tables, and operational reporting.
* Easy to query structured, relational data.
* Suitable for lightweight transactional writes.
**Cons:**
* Not optimized for large batch reads/writes.
* Limited parallelism compared to data lake storage.
* Higher cost for data movement at scale.
---
### โ
**Best Practice Suggestion**
* **Use ADLS Gen2** as your **primary data lake** for raw and processed data.
* **Connect to Azure SQL** for:
* pulling reference data (dimensions, lookup tables),
* writing back small outputs (like reporting metrics or curated outputs).
Combining both is often the most robust architecture.
