Forward:

https://www.superoutlier.tech/databricks-cluster-vs-sql-warehouses/

If you are using a Databricks premium account, you see SQL personal along with Data Engineering and Machine Learning.

If you are using Data Engineering or Machine Learning, you will be launching Clusters (Interactive or Job), but when you are using SQL Persona, you will notice SQL Warehouse (a.k.a) SQL Endpoints instead of standard Databricks clusters.

This article quickly summarizes the differences between a Databricks Cluster and SQL Warehouse.

Databricks Engineering Cluster

Databricks Engineer Cluster is a cloud-based data processing platform that is designed for big data processing and analytics. It offers a unified analytics engine that supports different types of workloads. Databricks Engineer Cluster can handle both structured and unstructured data and can be integrated with other cloud services, such as Azure Data Lake Storage, Azure Blob Storage, and Amazon S3. There are some summary below:

• Databricks Engineer Cluster is built on top of Apache Spark, an open-source distributed computing engine that is optimized for processing large-scale data.
• The platform uses a cluster-based architecture, where multiple virtual machines are connected to form a cluster. The size and configuration of the cluster can be adjusted based on the workload requirements.
• Databricks Engineer Cluster supports multiple programming languages, including Python, Scala, R, and SQL. The platform provides a notebook interface that allows users to write and execute code in a collaborative environment.
• The platform supports both batch processing and real-time processing. For batch processing, users can submit Spark jobs that process data in bulk. For real-time processing, users can create streaming applications that process data as it arrives.
• Databricks Engineer Cluster can be integrated with other cloud services, such as Azure Data Lake Storage, Azure Blob Storage, and Amazon S3. It also supports data sources such as Hadoop Distributed File System (HDFS), Apache Cassandra, and Apache Kafka.

Databricks SQL Data warehouse

SQL Data Warehouse Cluster is a cloud-based data warehousing solution that is designed for large-scale data analytics and reporting.

• SQL Data Warehouse Cluster is built on top of the Azure SQL Database, a cloud-based relational database management system (RDBMS) that is optimized for handling large datasets.
• The solution uses a distributed architecture, where multiple nodes are connected to form a cluster. The size and configuration of the cluster can be adjusted based on the workload requirements.
• SQL Data Warehouse Cluster supports the T-SQL language, which is a variant of SQL that is optimized for handling large datasets and complex queries. The solution provides a web-based interface, called Azure Portal, that allows users to manage and monitor the cluster.
• SQL Data Warehouse Cluster is optimized for handling large datasets and can scale up to petabytes of data. The solution uses a columnstore index, which is a type of index that is optimized for handling analytical queries.
• SQL Data Warehouse Cluster can be integrated with other Azure services, such as Azure Data Factory and Azure Analysis Services, to create end-to-end analytics solutions. It also supports data sources such as Azure Blob Storage, Azure Data Lake Storage, and SQL Server.

What is difference between them

• SQL Data Warehouse Cluster is designed for executing SQL commands, while Clusters are built to execute a wide range of commands, including Scala, R, Python, and SQL.
• One of the key benefits of SQL Data Warehouse Cluster is that it eliminates the overhead of managing libraries such as JAR, PIP, or WHL. On the other hand, Clusters can become overloaded with libraries, which can impact performance.
• SQL Data Warehouse Cluster simplifies SQL endpoint management and accelerates launch times. In contrast, Cluster configuration can be complex for beginners.
• When it comes to scaling, SQL Data Warehouse Cluster scales up and down as a Cluster. On the other hand, Cluster scaling is based on nodes, and it can scale up to the maximum range.
• SQL Data Warehouse Cluster has a Serverless feature (Private Preview) that significantly reduces start time, which is not currently available for Clusters.

The next item is not a difference, it's just a feature available in both.

Both SQL Data Warehouse Cluster and Databricks Engineering Cluster can be used to connect to BI tools like Tableau and have Auto Start capability.