10+ Pyspark Developer Interview Questions and Answers

Interview questions for a PySpark Developer

• Explained current project and its implementation

• Differentiated between managed and external table

• Described Spark architecture and RDD

• Discussed characteristics of RDD and lazy nature

• Provided insert statement for managed and external table

• Explained deployment related to code in PySpark

• Answered Python related questions

• Explained how to convince manager/scrum master for code changes

• Discussed team size and management

Coalesce reduces the number of partitions without shuffling data, while repartition increases the number of partitions by shuffling data. Cache and persist are used to persist RDDs in memory.

• Coalesce is used to reduce the number of partitions without shuffling data, while repartition is used to increase the number of partitions by shuffling data.

• Coalesce is more efficient when reducing partitions as it avoids shuffling, while repartition involves shuffling data across the net...read more

Orchestrating code in GCP involves using tools like Cloud Composer or Cloud Dataflow to schedule and manage workflows.

• Use Cloud Composer to create, schedule, and monitor workflows using Apache Airflow

• Utilize Cloud Dataflow for real-time data processing and batch processing tasks

• Use Cloud Functions for event-driven serverless functions

• Leverage Cloud Scheduler for job scheduling

• Integrate with other GCP services like BigQuery, Pub/Sub, and Cloud Storage for data processing and s...read more

The SQL code for calculating year-on-year growth percentage with year-wise grouping.

• Use the LAG function to get the previous year's value

• Calculate the growth percentage using the formula: ((current year value - previous year value) / previous year value) * 100

• Group by year to get year-wise growth percentage

To connect Google Cloud Platform with Apache Spark, tools like Dataproc, Cloud Storage, and BigQuery can be used.

• Use Google Cloud Dataproc to create managed Spark and Hadoop clusters on GCP.

• Store data in Google Cloud Storage and access it from Spark applications.

• Utilize Google BigQuery for querying and analyzing large datasets directly from Spark.

Spark architecture is based on a master-slave model with a cluster manager and a distributed file system.

• Spark has a driver program that communicates with a cluster manager to coordinate tasks.

• The cluster manager allocates resources to worker nodes, which execute tasks in parallel.

• Spark uses a distributed file system, such as HDFS, to store and access data across the cluster.

• Spark also includes a variety of libraries and APIs for data processing, machine learning, and streami...read more

Azure linked services are connections to external data sources, while Azure datasets are structured data objects within Azure Data Factory.

• Azure linked services are used to connect to external data sources such as databases, storage accounts, and SaaS applications.

• Azure datasets are structured data objects within Azure Data Factory that represent data from linked services or other sources.

• Linked services define the connection information and credentials required to connect to...read more

Actions and transformations are two types of operations in PySpark. Actions return a value to the driver program, while transformations create a new RDD.

• Actions are operations that trigger the execution of the Spark job, such as collect(), count(), and saveAsTextFile().

• Transformations are operations that create a new RDD from an existing one, such as map(), filter(), and reduceByKey().

• Actions are lazy operations, meaning they are not executed until an action is called, while ...read more

RDD is a distributed collection of objects, while DataFrame is a distributed collection of data organized into named columns.

• RDD is more low-level and requires manual schema definition, while DataFrame provides a higher-level API with schema inference.

• DataFrames are optimized for processing structured data, while RDDs are more suitable for unstructured data.

• DataFrames support SQL queries and optimizations through Catalyst optimizer, while RDDs do not have built-in optimizatio...read more

A stage is a collection of tasks that perform a specific computation, while a task is a unit of work that is executed on a single executor.

• Stage is a higher-level unit of work that can be broken down into multiple tasks.

• Tasks are individual units of work that are executed on a single executor.

• Stages are used to organize and coordinate tasks in a Spark job.

• Tasks are the actual units of computation that are performed by executors.

• Example: A stage may involve reading data from a...read more

Spark is used for big data processing due to its speed, scalability, and ease of use.

• Spark is used for processing large volumes of data quickly and efficiently.

• It offers in-memory processing which makes it faster than traditional MapReduce.

• Spark provides a wide range of libraries for diverse tasks like SQL, streaming, machine learning, and graph processing.

• It can run on various platforms like Hadoop, Kubernetes, and standalone clusters.

• Spark's ease of use and compatibility wi...read more

Transformations are lazy operations that create new RDDs, while Actions are operations that trigger computation and return results.

• Transformations are operations like map, filter, and reduceByKey that create a new RDD from an existing one.

• Actions are operations like count, collect, and saveAsTextFile that trigger computation on an RDD and return results.

• Transformations are lazy and are only executed when an action is called, allowing for optimization of computations.

• Actions a...read more

A program to count the occurrences of each word in a text document.

• Use Spark RDD to read the text file and split the lines into words

• Apply transformations like map and reduceByKey to count the occurrences of each word

• Handle punctuation and case sensitivity to ensure accurate word count results