Dish Network Data Engineer Interview Questions and Answers

Python and SQL Questions (Easy to Medium)

Q1. Project Work Discussion
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Q2. SQL Coding (Can be with an online compiler)
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Q3. Python Coding (Can be with an online compiler)
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Q4. PySpark Coding (All the coding answers have to be submitted to the interviewer)
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Q5. PySpark Joins, and Databricks, and Vanilla Spark difference
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Q1. SQL 2 Questions (Easy to Medium) (Maybe on the compiler)
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Q2. Python (Easy) (Maybe on compiler)
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Q3. Scenario-based questions on the project
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Q1. Sql question related to windows function
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Q2. Unix basics - find a word in a set of files
Ans. 
Using Unix command 'grep' to find a word in a set of files
• Use 'grep' command followed by the word you want to search for and the file(s) you want to search in

• Add the '-r' flag to search recursively in all files in a directory

• Use '-i' flag for case-insensitive search

• Example: grep 'hello' file.txt

• Example: grep -r 'error' /path/to/directory

• Example: grep -i 'apple' file1.txt file2.txt
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Q3. Python question related to file system, regex
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Basic aptitude test asking questions very basic

Coding questions based on dsa

Q1. Basic hr question
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Q1. What is the difference between group by and partition by
Ans. 
Group by is used to group rows based on a column, while partition by is used to divide rows into partitions based on a column.
• Group by is used with aggregate functions to summarize data

• Partition by is used to perform window functions on each partition separately

• Group by creates a single result set, while partition by can create multiple result sets

• Group by is used in SQL, while partition by is used in window functions
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Resume short listing

2 coding questions easy with aptitude questions as well

Based on your job description coding questions will be asked in different categories. If you are able to manage time. It is easy to crack

Q1. Optimization techniques in pyspark
Ans. 
Optimization techniques in pyspark involve partitioning, caching, and using efficient transformations.
• Partitioning data to distribute workload evenly

• Caching frequently accessed data to avoid recomputation

• Using efficient transformations like map, filter, and reduce

• Avoiding unnecessary shuffling of data
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Q2. Few coding questions. Find the first unique character in string
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Q1. It is bit difficult for me. Mention your work experience
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Q1. Normal questions on spark, hadoop, sql
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Q2. One python coding question was there
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Q1. Python coding felt difficult, its a moderate difficult question
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Q2. Questions on project, sql moderate questions
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Q1. Spark and SQL and previous experience
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Q2. Scenario based questions
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Q1. Project architect
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