When comparing AWS Glue Streaming and AWS Lambda for real-time ETL (Extract, Transform, Load) processes, there are several factors to consider: batch windows, cost, scaling, and specific interview-style scenarios. Here’s a detailed comparison based on these aspects:

### Batch Windows
– **AWS Glue Streaming**: Glue Streaming jobs can handle continuous data ingestion in micro-batches. The minimum batch window for a Glue Streaming job is 1 second, which allows for near real-time processing. This is suitable when you need to perform complex transformations on data streams as they flow through the system.

– **AWS Lambda**: Lambda is event-driven, triggering functions in response to incoming data. While it can process each event almost in real-time, it’s not designed to handle data in batches unless combined with services like Kinesis or SQS, which can group events before invoking your Lambda function.

### Cost
– **AWS Glue Streaming**: Glue pricing is based on the amount of data processed and the compute resources consumed. It can be more cost-effective for jobs that require continuous processing and significant compute resources, thanks to Glue’s ability to scale horizontally.

– **AWS Lambda**: Lambda is priced based on the number of requests and the duration of execution time (i.e., how long your code runs), with a free tier available. While it can be cost-effective for low to moderate workloads, costs can increase quickly with high-frequency invocations and long-running processes, especially if you exceed the free tier limits.

### Scaling
– **AWS Glue Streaming**: Offers built-in scaling capabilities where the job can automatically adjust its resources to accommodate the incoming data load. It’s ideal for handling variability in throughput over time without manual intervention.

– **AWS Lambda**: Automatically scales to accommodate traffic, supporting up to thousands of concurrent executions per region. However, it has account-level concurrency limits, which you need to manage and might require manual adjustment.

### Interview-Style Scenarios

**Scenario 1: High Availability and Consistent Throughput**
– **Glue Streaming** would be preferred here, as it allows for consistent processing of data streams with fault tolerance and consistency. It’s particularly well-suited if the ETL process involves complex transformations that need to be run continuously without interruption.

**Scenario 2: Serverless Architecture with Event-Driven Processing**
– **AWS Lambda** excels in scenarios where you need a serverless way to handle events or messages independently. It’s optimal when you are seeking an architecture with minimal infrastructure management.

**Scenario 3: Handling Large Volumes of Data in Real-Time**
– **AWS Glue Streaming** is more appropriate if the system is expected to handle a stable stream of large data volumes with complex processing. Glue’s underlying use of Apache Spark ensures that it can handle stateful transformations over large datasets effectively.

**Scenario 4: Low Latency Requirements with Sporadic Bursts in Traffic**
– **AWS Lambda** fits well when low-latency execution of simple logic on sporadic data bursts is necessary. The near-instantaneous execution and event-driven nature make it ideal for scenarios like real-time analytics on user interactions where each event triggers independently.

**Scenario 5: Budget Constraint with Efficient Process Handling**
– **AWS Lambda**, with its pricing model based on execution time and invocations, allows for cost-efficient processing for workloads where execution time is short and invocations are within free tier limits.

**Concluding Thoughts:** The decision between AWS Glue Streaming and AWS Lambda largely depends on the specific requirements of your ETL workload. Glue Streaming is suitable for continuous, high-throughput processing with complex transformations, whereas Lambda is ideal for event-driven architectures with concise transformations and intermittent data flows. Understanding the nature of your data, workload characteristics, and budget constraints will guide the choice between these two services for real-time ETL on AWS.