Which framework is integrated with Azure Databricks for advanced analytics and ML?

The most appropriate framework integrated with Azure Databricks for advanced analytics and machine learning is Apache Spark. Azure Databricks is built on top of Apache Spark and leverages its capabilities for big data processing and analytics. It provides a collaborative workspace, allowing data engineers and data scientists to develop and scale their machine learning models seamlessly.

Apache Spark offers a powerful distributed computing platform with built-in support for machine learning through its MLlib library. This integration enhances the ability to perform large-scale data processing, which is essential for training complex machine learning models on big datasets. With features like data streaming, SQL, and graph processing, Spark is well-suited for a variety of analytics and machine learning tasks.

While TensorFlow and Keras are popular frameworks for machine learning, they do not have the native integration and extensive support within the Azure Databricks environment as Apache Spark does. Apache Hadoop, while related to big data processing, is focused on storage and batch processing rather than real-time analytics and machine learning tasks, making it less relevant in this context.