Image Source

1. How do you deploy PySpark applications in a production environment?

2. What are some best practices for monitoring and logging PySpark jobs?

3. How do you manage resources and scheduling in a PySpark application?

4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results).

5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark.

6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark?

7. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue.

8. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join?

9. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data.

10. You are tasked with processing real-time sensor data to detect anomalies. Explain the steps you would take to implement this using PySpark.

11. Describe how you would design and implement an ETL pipeline in PySpark to extract data from an RDBMS, transform it, and load it into a data warehouse.

12. Given a requirement to process and transform data from multiple sources (e.g., CSV, JSON, and Parquet files), how would you handle this in a PySpark job?

13. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this.

14. Describe how you would use PySpark to join data from a Hive table and a Kafka stream.

15. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this.

Happy learning 😊