Analysis of the Question

The question requires recommending a solution for analyzing intrusion detection data using Apache Spark analytics with minimal administrative overhead. The key requirements are:

• Apache Spark analytics capability - The solution must support Spark-based processing
• Intrusion detection analysis - Processing network and system activity data for security threats
• Minimal administrative efforts - The solution should be managed or fully managed to reduce operational overhead

Evaluation of Options

Option D: Azure Databricks ✅

Azure Databricks is the optimal choice because:

• Fully managed Apache Spark platform - Provides a fully managed Spark environment with automated cluster management, scaling, and maintenance
• Built for advanced analytics - Specifically designed for data engineering, data science, and machine learning workloads
• Security and compliance - Offers enhanced security monitoring, audit logging, and compliance features suitable for intrusion detection
• Minimal administrative overhead - Automated cluster management, patching, and optimization reduce operational burden
• Integration capabilities - Seamlessly integrates with Azure Data Lake Storage and other Azure services for data ingestion

Option A: Azure HDInsight ❌

While HDInsight supports Spark clusters, it requires more administrative overhead for cluster management, monitoring, and maintenance compared to Databricks.

Option B: Azure Data Factory ❌

Data Factory is primarily an ETL/orchestration service, not an analytics platform. It doesn't provide native Spark analytics capabilities for intrusion detection analysis.

Option C: Azure Data Lake Storage ❌

This is a storage service, not an analytics platform. While it can store the intrusion detection data, it cannot perform the required Spark analytics.

Conclusion

Azure Databricks provides the most comprehensive solution that aligns with all requirements: native Apache Spark support for analytics, security monitoring capabilities for intrusion detection, and minimal administrative overhead through its fully managed service model.