How does Databricks primarily handle big data analytics?

Databricks primarily handles big data analytics through the utilization of Apache Spark's distributed computing capabilities. Apache Spark is designed to process large volumes of data quickly by distributing the data and computational tasks across a cluster of machines. This parallel processing allows for efficient handling of big data, enabling tasks such as data transformation, aggregation, and analysis to be performed at scale.

The architecture of Spark ensures that it can process data in a fault-tolerant manner with the support of in-memory computing, which significantly speeds up data processing compared to disk-based systems. This capability is critical in big data scenarios where performance, scalability, and processing efficiency are key requirements.

Using SQL databases or traditional data warehousing solutions can limit the scalability and performance needed for very large datasets, as they are not inherently designed for distributed processing. While employing machine learning models plays a vital role in deriving insights from data, it is primarily the infrastructure and processing capabilities provided by Spark that enables effective big data analytics in Databricks.