Fractal Analytics Senior Data Engineer Interview Questions, Process, and Tips

Q1. Employee table has Emp id and gender. Write an update statement to update female to male and viceversa.
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Q2. Basic PySpark and Databricks questions - Unity catalog, parquet vs delta, Databricks workflows, merge command in PySpark. Activities used in ADF.
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Q1. Interviews question about adf adls gen2 ,databricks and experience
Ans. One might ask you on how can one configure these
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Q2. Transformations in databricks
Ans. 
Transformations in Databricks involve manipulating data using functions like map, filter, reduce, etc.
• Transformations are operations that are applied to RDDs in Databricks

• Common transformations include map, filter, reduce, flatMap, etc.

• Transformations are lazy evaluated and create a new RDD

• Example: map transformation to convert each element in an RDD to uppercase
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Q3. Concepts in spark
Ans. 
Spark is a distributed computing framework for processing big data.
• Spark is built around the concept of Resilient Distributed Datasets (RDDs)

• It supports various programming languages like Scala, Java, Python, and R

• Spark provides high-level APIs like DataFrames and Datasets for structured data processing

• It includes libraries for SQL, streaming, machine learning, and graph processing

• Spark can run on various cluster manag
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Q1. In depth discussion on projects
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Q2. Performance enhancemnets in pyspark
Ans. 
Performance enhancements in PySpark involve optimizing code, tuning configurations, and utilizing efficient data structures.
• Use partitioning to distribute data evenly across nodes

• Cache intermediate results to avoid recomputation

• Optimize joins by broadcasting smaller tables

• Use efficient data formats like Parquet or ORC

• Tune Spark configurations for memory and parallelism
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Q3. Data handling and data quality
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It was a hackerearth test ranging from simple to intermediate

Q1. Basic on Pyspark, Hive
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Q2. Basic on SQL related
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Q1. It was tougher round with coding questions
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Language coding test which might be very specific to the proficient for data technologies

Q1. More of basic understanding on the concepts of processing of data and related technologies
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Q1. In terms of architecture understanding and designing perspective
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