CitiusTech Azure Data Engineer Interview Questions and Answers

Performance optimization techniques in Pyspark involve partitioning, caching, and using efficient transformations.

• Partitioning data to distribute workload evenly

• Caching intermediate results to avoid recomputation

• Using efficient transformations like map, filter, and reduce

• Avoiding unnecessary shuffling of data

Types of clusters in Databricks include Standard, High Concurrency, and Single Node clusters.

• Standard clusters are used for general-purpose workloads

• High Concurrency clusters are optimized for concurrent workloads

• Single Node clusters are used for development and testing purposes

Catalyst optimizer is a query optimizer in Apache Spark that leverages advanced techniques to optimize and improve the performance of Spark SQL queries.

• Catalyst optimizer uses a rule-based and cost-based optimization approach to generate an optimized query plan.

• It performs various optimizations such as constant folding, predicate pushdown, and projection pruning to improve query performance.

• Catalyst optimizer also leve...read more

Explode function is used in Apache Spark to split an array into multiple rows.

• Used in Apache Spark to split an array into multiple rows

• Creates a new row for each element in the array

• Commonly used in data processing and transformation tasks

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Delta Lake provides ACID transactions, schema enforcement, and time travel capabilities on top of data lakes.

• Data lakes are a storage repository that holds a vast amount of raw data in its native format until it is needed.

• Delta Lake is optimized for big data workloads and provides reliability and performance ...read more

RDD stands for Resilient Distributed Dataset, a fundamental data structure in Apache Spark.

• RDD is a fault-tolerant collection of elements that can be operated on in parallel.

• RDDs are immutable, meaning they cannot be changed once created.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (returning a value to the driver program).

Performance optimization techniques in Pyspark involve partitioning, caching, and using efficient transformations.

• Partitioning data to distribute workload evenly

• Caching intermediate results to avoid recomputation

• Using efficient transformations like map, filter, and reduce

• Avoiding unnecessary shuffling of data