Certainly! Here are several interview-style prompts specifically for batch engineering:

1. **Design a Data Lake:**
– How would you design a data lake architecture for a retail company that collects data from multiple channels such as in-store, online, and mobile applications?
– Describe the steps you would take to ensure data quality and consistency in a data lake environment.
– Discuss how you would implement data governance and security in a data lake.

2. **Optimize AWS Glue Jobs:**
– Describe the process you would follow to optimize an AWS Glue ETL job that is processing large volumes of data.
– What techniques would you use to reduce the execution time of Glue jobs?
– Explain how to manage schema evolution and partitioning strategy in AWS Glue for efficiently querying data.

3. **Compare EMR vs Redshift:**
– Compare and contrast the use cases for AWS EMR and Amazon Redshift in the context of batch processing.
– What factors would influence your decision to choose EMR over Redshift for a big data processing project?
– Discuss the cost implications and scaling capabilities of using EMR compared to Redshift.

4. **Design an ETL Pipeline:**
– How would you design an ETL pipeline to process daily transaction data for a financial services company?
– What tools and technologies would you choose for building a scalable and fault-tolerant ETL pipeline?
– Describe how you would handle errors and exceptions during the ETL process.

5. **Data Partitioning Strategies:**
– Explain the importance of data partitioning in batch processing workflows.
– What criteria would you use to determine how data should be partitioned in a large-scale distributed system?
– Provide examples of partitioning strategies and evaluate their effectiveness in different scenarios.

6. **Batch Processing vs Stream Processing:**
– Discuss the pros and cons of batch processing compared to stream processing.
– Provide examples of applications best suited for batch processing.
– How would you transition a batch processing system to a near-real-time or stream processing system?

7. **Performance Tuning of Hadoop Jobs:**
– What are some common techniques for tuning the performance of Hadoop jobs?
– How would you diagnose and fix performance bottlenecks in a Hadoop-based batch processing system?
– Discuss the impact of data skew and how you would address it in your jobs.

8. **Scalability and Reliability:**
– How do you ensure that a batch processing system is both scalable and reliable?
– Describe a situation where you had to scale a batch processing workflow to accommodate growing data volumes.
– What tools or practices would you implement to monitor and maintain the reliability of batch jobs?

9. **Data Schema Design:**
– How would you design an efficient data schema for storing and processing large datasets in a batch processing system?
– Discuss the trade-offs between denormalization and normalization for batch processing workloads.
– What considerations would you take into account for schema evolution over time?

10. **Data Cleaning and Transformation:**
– Describe your approach to data cleaning and transformation in a batch processing pipeline.
– What tools and frameworks do you prefer for handling complex data transformations?
– How do you balance accuracy and performance when implementing data cleaning operations?

These prompts should help gauge a candidate’s technical expertise and thought process related to batch engineering.