N+A fresher Interview Questions and Answers

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Q1. Sql joins, window functions
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Q2. Writing some queries in front of interviewer
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Q1. What is lazy evaluation in spark.
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Lazy evaluation in Spark delays the execution of transformations until an action is called.
• Lazy evaluation allows Spark to optimize the execution plan by combining multiple transformations into a single stage.

• Transformations are not executed immediately, but are stored as a directed acyclic graph (DAG) of operations.

• Actions trigger the execution of the DAG and produce results.

• Example: map() and filter() are transformat...read more
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Q2. What is mapreduce
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MapReduce is a programming model and processing technique for parallel and distributed computing.
• MapReduce is used to process large datasets in parallel across a distributed cluster of computers.

• It consists of two main functions - Map function for processing key/value pairs and Reduce function for aggregating the results.

• Popularly used in big data processing frameworks like Hadoop for tasks like data sorting, searching...read more
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Q1. What is skewness and skewd tables
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Skewness is a measure of asymmetry in a distribution. Skewed tables are tables with imbalanced data distribution.
• Skewness is a statistical measure that describes the asymmetry of the data distribution around the mean.

• Positive skewness indicates a longer tail on the right side of the distribution, while negative skewness indicates a longer tail on the left side.

• Skewed tables in data engineering refer to tables with imba...read more
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Q1. What is spark and explain working
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Spark is a distributed computing framework designed for big data processing.
• Spark is built around the concept of Resilient Distributed Datasets (RDDs) which allow for fault-tolerant parallel processing of data.

• It provides high-level APIs in Java, Scala, Python, and R for ease of use.

• Spark can run on top of Hadoop, Mesos, Kubernetes, or in standalone mode.

• It includes modules for SQL, streaming, machine learning, and gra...read more
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Q1. PartitionBy vs bucketBy in spark
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Q1. Explain error handling in PySpark
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Error handling in PySpark involves using try-except blocks and logging to handle exceptions and errors.
• Use try-except blocks to catch and handle exceptions in PySpark code

• Utilize logging to record errors and exceptions for debugging purposes

• Consider using the .option('mode', 'PERMISSIVE') method to handle corrupt records in data processing
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Q1. Data Warehousing related questions
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Q1. Data Modelling related questions
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