Answer: Azure Databricks

## Detailed Explanation ### Requirements Analysis The question specifies three key requirements: 1. **Streaming data processing** from Apache Kafka source 2. **Output to Azure Data Lake Storage Gen2** 3. **Java programming language support** for the development team ### Evaluation of Options **D. Azure Databricks** - **✓ OPTIMAL CHOICE** - **Apache Spark Integration**: Azure Databricks provides a fully managed Apache Spark platform, which has excellent Kafka integration through Spark Structured Streaming - **Java Support**: Full Java SDK and API support for stream processing, allowing developers to write streaming jobs in Java - **Kafka Connectivity**: Direct Kafka connector for reading streaming data from Kafka topics - **ADLS Gen2 Integration**: Native support for writing processed data to Azure Data Lake Storage Gen2 - **Streaming Capabilities**: Supports stateful aggregations, windowing operations, and complex event processing - **Enterprise Features**: Provides monitoring, scaling, and enterprise-grade security features **A. Azure Event Hubs** - **✗ NOT SUITABLE** - Primarily an event ingestion service, not a stream processing engine - While it can receive events, it doesn't provide native stream processing capabilities - Limited to basic event routing and doesn't support complex aggregations **B. Azure Data Factory** - **✗ NOT SUITABLE** - Primarily an ETL/ELT orchestration service for batch processing - Limited streaming capabilities and not designed for real-time stream processing - Poor fit for continuous aggregation of streaming data from Kafka **C. Azure Stream Analytics** - **✗ NOT SUITABLE** - Uses SQL-like query language for stream processing, not Java - Limited Java integration and doesn't leverage the team's Java proficiency - While it can process streaming data, it doesn't align with the Java development requirement ### Why Azure Databricks is the Best Choice Azure Databricks with Apache Spark Structured Streaming provides: - **Java-native development** using Spark's Java APIs - **Robust Kafka integration** for reading streaming data - **Powerful aggregation capabilities** with windowing and state management - **Seamless ADLS Gen2 integration** for output storage - **Enterprise reliability** with managed infrastructure and monitoring This combination ensures the development team can leverage their Java expertise while building a robust, scalable streaming solution that meets all specified requirements.

Author: LeetQuiz Editorial Team