Cognizant Pyspark Developer Interview Questions and Answers

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

SQL query to find the second highest rank in a dataset

• Use the ORDER BY clause to sort the ranks in descending order

• Use the LIMIT and OFFSET clauses to skip the highest rank and retrieve the second highest rank

• Example: SELECT rank FROM dataset ORDER BY rank DESC LIMIT 1 OFFSET 1

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.

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

Optimization techniques in Apache Spark improve performance and efficiency.

• Partitioning data to distribute work evenly

• Caching frequently accessed data in memory

• Using broadcast variables for small lookup tables

• Optimizing shuffle operations by reducing data movement

• Applying predicate pushdown to filter data early

Coalesce reduces the number of partitions without shuffling data, while repartition reshuffles data to create a specific number of partitions.

• Coalesce is used to reduce the number of partitions without shuffling data

• Repartition is used to increase or decrease the number of partitions by shuffling data

• Coalesce is more efficient when reducing partitions as it avoids shuffling

• Repartition is useful when you need to explicitly control the number of partitions

• Example: coalesce(5) v...read more