LTIMindtree Data Engineer Interview Questions, Process, and Tips

Sql question easy to hard

Q1. Sql questions and concepts
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Python and sql code , handling different file formats

Q1. Aws architecture
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Q2. Pyspark code about handling different file formats
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Using PySpark to handle different file formats
• Use PySpark's built-in functions to read and write different file formats such as CSV, Parquet, JSON, etc.

• Specify the file format when reading data using PySpark's read method, for example: spark.read.format('csv').load('file.csv')

• When writing data, specify the file format using PySpark's write method, for example: df.write.format('parquet').save('file.parquet')
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Q1. Spark join syntax
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Q1. Word Count program in Spark Scala
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Implement a Word Count program in Spark Scala
• Use Spark's RDD API to read input text file

• Split each line into words and map them to key-value pairs

• ReduceByKey operation to count occurrences of each word

• Save the result to an output file
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Q2. Apache spark architecture
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Q3. Higher Order Functions in Scala
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Higher Order Functions in Scala are functions that take other functions as parameters or return functions as results.
• Higher Order Functions allow for more concise and readable code.

• Examples include map, filter, reduce, and flatMap in Scala.

• They promote code reusability and modularity.

• Higher Order Functions are a key feature of functional programming.
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Q1. Scenerio based ques
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Q2. Unix and shell scripting question
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Q1. Challenges faced in project
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One of the challenges faced in the project was managing large volumes of data efficiently.
• Handling data ingestion and processing at scale

• Optimizing data storage and retrieval

• Ensuring data quality and consistency

• Implementing efficient data pipelines

• Dealing with real-time data streaming challenges
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Q2. Strengths and weakness
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Q1. Strength and weakness
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