EPAM Systems Data Engineer Interview Questions, Process, and Tips

Q1. Spark architecture, optimisations
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Q2. Spark & Python Coding
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Q1. Tell me about yourself
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Q2. What tech stack are used
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The tech stack used includes Python, SQL, Apache Spark, Hadoop, AWS, and Docker.
• Python for data processing and analysis

• SQL for database querying

• Apache Spark for big data processing

• Hadoop for distributed storage and processing

• AWS for cloud services

• Docker for containerization
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Q1. Talk about your past experiences
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Q2. Types of Variables in Scala
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Scala has two types of variables - mutable and immutable.
• Scala has mutable variables that can be reassigned using the var keyword.

• Scala also has immutable variables that cannot be reassigned once they are initialized using the val keyword.

• Example: var mutableVariable = 10; val immutableVariable = 20;
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Q3. Explained in Detail about next Steps. Total 5 Rounds Including HR and HackerRank Test Round 1: HR Round 2: hackerrank assessment - If we clear this we move to Next Round Round 3: Technical Interview - Incl...read more
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Hacker Rank Assessment - take home

Q1. This round was scheduled for 1.5 hours and lasted 1 hrs 5 minutes. Discuss about projects done for previous company and architecture of the same.
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Q2. Write code for printing duplicate numbers in a list.
Ans. 
This code identifies and prints duplicate numbers from a given list using a dictionary to track occurrences.
• Use a dictionary to count occurrences of each number.

• Iterate through the list and update the count in the dictionary.

• Print numbers that have a count greater than 1.

• Example: For the list [1, 2, 3, 2, 4, 3], the output should be 2 and 3.
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Q3. Scala traits, higher order functions, currying
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Q4. Connecting Spark to Azure SQL Database.
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Connecting Spark to Azure SQL Database involves configuring JDBC and using Spark's DataFrame API for data operations.
• Use the JDBC driver for Azure SQL Database to establish a connection.

• Example connection string: 'jdbc:sqlserver://<server>.database.windows.net:1433;database=<database>;user=<user>@<server>;password=<password>'

• Utilize Spark's DataFrame API to read and write data: df.write.jd...read more
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Q5. Elaboration of Spark optimization techniques. Types of transformations, shuffling.
Ans. 
Spark optimization techniques enhance performance through efficient data processing and resource management.
• Use DataFrames and Datasets for optimized execution plans.

• Leverage lazy evaluation to minimize unnecessary computations.

• Apply partitioning to distribute data evenly across nodes, e.g., using 'repartition' or 'coalesce'.

• Minimize shuffling by using narrow transformations like 'map' and 'filter' instead of wide tran...read more
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Q6. Difference between cache and persist, repartition and coalesce.
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Cache stores data in memory for quick access, while persist saves it to disk. Repartition changes data distribution; coalesce reduces partitions.
• Cache: Stores DataFrame in memory for faster access during subsequent operations.

• Persist: Saves DataFrame to disk, allowing for fault tolerance but slower than cache.

• Repartition: Increases or decreases the number of partitions, potentially shuffling data across nodes.

• Coalesce:...read more
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Q7. Spark components and job execution steps.
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Q8. Hive types of tables and difference between them
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Hive supports two types of tables: Managed and External, each with distinct data management and storage characteristics.
• Managed Tables: Hive manages both the schema and the data. Dropping the table deletes the data.

• External Tables: Hive manages only the schema. Dropping the table does not delete the data, which remains in the external storage.

• Use Managed Tables for temporary data that can be recreated easily.

• Use Extern...read more
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Q9. This was the final round of 1 hour and lasted 45 minutes.I was asked technical questions along with last companies project description.
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Q10. Discuss project and it's architecture.
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Q11. Write code to print reverse of a sentence word by word.
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Q12. Difference between RDD, Dataframe, Dataset.
Ans. 
RDD, DataFrame, and Dataset are core abstractions in Apache Spark for handling distributed data processing.
• RDD (Resilient Distributed Dataset) is the fundamental data structure in Spark, representing an immutable distributed collection of objects.

• DataFrames are similar to RDDs but are optimized for performance and allow for schema-based operations, making them easier to use.

• Datasets combine the benefits of RDDs and Dat...read more
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Q13. Lineage graph, DAG formation, RDDs characteristics
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Q14. Two coding questions on codility. One was easy and second medium. 10 MCQ questions on Big Data related technologies.
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Q1. Asked basic big data related questions. Hadoop, spark arch. Spark optimization, serialization. Hadoop datanode, namenode. SQL queries medium level.
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Q1. How will you handle data skewness in spark
Ans. 
Data skewness can be handled in Spark by using techniques like partitioning, bucketing, and broadcasting.
• Partitioning the data based on a key column can distribute the data evenly across the cluster.

• Bucketing can further divide the data into smaller buckets based on a hash function.

• Broadcasting small tables can reduce the amount of data shuffled across the network.

• Using dynamic allocation can also help in handling data...read more
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