CodeAstu Technologies Data Engineer Interview Questions and Answers

Q1. What is PySpark, and can you explain its features and uses?
Ans. 
PySpark is a Python API for Apache Spark, used for big data processing and analytics.
• PySpark is a Python API for Apache Spark, a fast and general-purpose cluster computing system.

• It allows for easy integration with Python libraries and provides high-level APIs in Python.

• PySpark can be used for processing large datasets, machine learning, real-time data streaming, and more.

• It supports various data sources such as HDFS, ...read more
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Q2. What is the difference between PySpark and Python?
Ans. 
PySpark is a Python API for Apache Spark, while Python is a general-purpose programming language.
• PySpark is specifically designed for big data processing using Spark, while Python is a versatile programming language used for various applications.

• PySpark allows for distributed computing and parallel processing, while Python is primarily used for sequential programming.

• PySpark provides libraries and tools for working wit...read more
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Q1. Write code in regular expression to remove the special characters
Ans. 
Use regular expression to remove special characters from a string
• Use the regex pattern [^a-zA-Z0-9\s] to match any character that is not a letter, digit, or whitespace

• Use the replace() function in your programming language to replace the matched special characters with an empty string

• Example: input string 'Hello! How are you?' will become 'Hello How are you' after removing special characters
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Q2. Questions on resume
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Q1. What is Databricks, Internals of it, Optimization technique
Ans. 
Databricks is a unified analytics platform that provides a collaborative environment for data scientists, engineers, and analysts.
• Databricks is built on top of Apache Spark, providing a unified platform for data engineering, data science, and business analytics.

• Internals of Databricks include a cluster manager, job scheduler, and workspace for collaboration.

• Optimization techniques in Databricks include query optimizati...read more
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Q2. SQL questions of joins,group by
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Q1. Scenario based azure data factory questions
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Q2. Project structres, pyspark dataframes related
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General question around data engineering

Q1. Different apache tech and tools
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Q2. What is snowflake
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Q3. Coding questions on python
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Q1. About location preference
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Q2. Behavioural questions
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Q1. Python Lambda Function
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Q2. What are pods in Kubernetes
Ans. 
Pods are the smallest deployable units in Kubernetes, consisting of one or more containers.
• Pods are used to run and manage containers in Kubernetes

• Each pod has its own unique IP address within the Kubernetes cluster

• Pods can contain multiple containers that share resources and are scheduled together

• Pods are ephemeral and can be easily created, destroyed, or replicated

• Pods can be managed and scaled using Kubernetes contr
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Q1. What Volume of data have you handled in your POCs ?
Ans. 
I have handled terabytes of data in my POCs, including data from various sources and formats.
• Handled terabytes of data in POCs

• Worked with data from various sources and formats

• Used tools like Hadoop, Spark, and SQL for data processing
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Q2. When will you decide to use repartition and coalesce?
Ans. 
Repartition is used for increasing partitions for parallelism, while coalesce is used for decreasing partitions to reduce shuffling.
• Repartition is used when there is a need for more partitions to increase parallelism.

• Coalesce is used when there are too many partitions and need to reduce them to avoid shuffling.

• Example: Repartition can be used before a join operation to evenly distribute data across partitions for bette...read more
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Q3. How will you design/configure a cluster if you have given 10 petabytes of data.
Ans. 
Designing/configuring a cluster for 10 petabytes of data involves considerations for storage capacity, processing power, network bandwidth, and fault tolerance.
• Consider using a distributed file system like HDFS or object storage like Amazon S3 to store and manage the large volume of data.

• Implement a scalable processing framework like Apache Spark or Hadoop to efficiently process and analyze the data in parallel.

• Utilize...read more
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Its mandatory test even for experience people

Q1. Related to technology
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Q1. Very good discussion towards work culture, salary and all
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