Kratos Data Engineer Interview Questions and Answers

Q1. What is lazy evaluation in spark.
Ans. 
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
Ans. 
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
Ans. 
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
Ans. 
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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Q1. Advanced sql on ctl
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Q2. Python data structure
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Nice apptitude-20 min,snowflake,ython,sql

Q1. Godd basic sql and snowflake
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Q2. Basics good have great intefview
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Q1. Why RDBMS over Non RDBMs
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Coding questions on sql python and spark

Q1. Hacker rank code pair
Ans. 
Implement a function to pair elements of an array based on a given sum.
• Iterate through the array and check if the current element plus any other element equals the given sum.

• Use a hash set to store elements already visited to avoid duplicate pairs.

• Return an array of arrays containing the pairs that sum up to the given value.
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Q2. Pyspark questions
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Q1. Money you want for this position
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Q2. Why to choose this position and not another
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