Answer: Azure Stream Analytics

## Detailed Explanation ### Requirements Analysis: - **Streaming data from Azure IoT Hub**: The solution needs to process real-time data from IoT devices - **Anomaly detection for spikes and dips in time series data**: This requires specialized time series analysis capabilities - **Output to Azure Synapse Analytics**: The processed data needs to be sent to a data warehouse for further analysis - **Minimize development and configuration effort**: The solution should leverage managed services with built-in functionality ### Option Analysis: **A: Azure Databricks** - While Databricks can perform anomaly detection using ML libraries, it requires significant development effort - Would need custom code for time series analysis and anomaly detection algorithms - Not optimized for minimal development effort requirement - Better suited for complex machine learning scenarios requiring custom development **B: Azure Stream Analytics** ✅ - **Built-in anomaly detection functions**: ASA provides native `ANOMALYDETECTION` functions specifically designed for detecting spikes and dips in streaming data - **Seamless IoT Hub integration**: Direct input adapter for Azure IoT Hub with minimal configuration - **Native Synapse Analytics output**: Direct output connector to Azure Synapse Analytics - **Low-code approach**: SQL-like query language with built-in functions reduces development effort significantly - **Real-time processing**: Optimized for streaming data scenarios **C: Azure SQL Database** - Primarily a relational database, not designed for real-time streaming data processing - Lacks built-in anomaly detection capabilities for streaming data - Would require complex ETL processes and custom development - Not suitable for the streaming data and minimal development effort requirements ### Why Azure Stream Analytics is Optimal: 1. **Built-in Anomaly Detection**: ASA's `ANOMALYDETECTION` function is specifically designed for identifying spikes, dips, and slow trends in time series data 2. **Minimal Development**: The service requires only SQL-like queries with built-in functions, significantly reducing coding effort 3. **End-to-End Integration**: Seamlessly connects IoT Hub (input) to Synapse Analytics (output) with native connectors 4. **Managed Service**: No infrastructure management required, aligning with the minimal configuration requirement 5. **Real-time Capabilities**: Specifically designed for streaming data scenarios like IoT telemetry The combination of built-in anomaly detection functions, seamless integration with required services, and minimal development overhead makes Azure Stream Analytics the optimal choice for this scenario.