Data Engineer II Interview Questions and Answers

Key concepts in joining tables using PySpark

• Understanding the different types of joins: inner join, outer join, left join, right join

• Specifying the join condition using 'on' or 'using' clauses

• Handling duplicate column names after joining by aliasing or dropping columns

• Utilizing broadcast joins for small tables to improve performance

HDFS stands for Hadoop Distributed File System, a distributed file system designed to store and manage large amounts of data across multiple machines.

• HDFS is part of the Apache Hadoop project

• It is designed to be highly fault-tolerant and scalable

• Data is stored in blocks across multiple nodes in a cluster

• HDFS is commonly used for big data processing and analytics

Efficiently reading large datasets involves using optimized tools and techniques to handle data processing and storage.

• Use distributed computing frameworks like Apache Spark for parallel processing of large datasets.

• Leverage data formats like Parquet or ORC that support efficient columnar storage and compression.

• Implement data partitioning to read only relevant subsets of data, improving performance.

• Utilize streaming data processing with tools like Apache Kafka for real-time ...read more

Garbage Collection in Spark manages memory by reclaiming unused objects to optimize resource utilization and performance.

• Spark uses JVM's Garbage Collection to manage memory automatically.

• It identifies and removes objects that are no longer in use, freeing up memory.

• Types of Garbage Collection include Minor GC (for young generation) and Major GC (for old generation).

• Example: If an RDD is no longer referenced, its memory can be reclaimed during GC.

• Tuning GC settings can improv...read more