Publicis Data Engineer Interview Questions, Process, and Tips

Q1. SQL and tableau
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Online test on sql join sub query

Q1. Final tech round
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Q1. Spark Context vs Spark Session
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Spark Context is the entry point to any Spark functionality while Spark Session is a unified entry point for Spark 2.0+.
• Spark Context is the old entry point to Spark functionality.

• Spark Session is a unified entry point for Spark 2.0+.

• Spark Context is used to create RDDs, accumulators and broadcast variables.

• Spark Session is used to create DataFrames, execute SQL queries and read data from external sources.
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Q2. Repartitioning vs Coalesce
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Repartitioning increases partitions while Coalesce reduces partitions.
• Repartitioning shuffles data across the cluster and can be used to increase parallelism.

• Coalesce merges partitions without shuffling data and can be used to reduce overhead.

• Repartitioning is expensive and should be used sparingly.

• Coalesce is faster but may not be as effective as repartitioning in increasing parallelism.

• Both can be used to optimize da
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Q3. Sql query to find Second Highest Salary
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Sql query to find Second Highest Salary
• Use ORDER BY and LIMIT to select the second highest salary

• Use subquery to select the maximum salary and exclude it from the result set

• Handle cases where there are ties for the highest salary
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Q4. Internal Working of Spark
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Spark is a distributed computing engine that processes large datasets in parallel across a cluster of computers.
• Spark uses a master-slave architecture with a driver program that coordinates tasks across worker nodes.

• Data is stored in Resilient Distributed Datasets (RDDs) that can be cached in memory for faster processing.

• Spark supports multiple programming languages including Java, Scala, and Python.

• Spark can be used f...read more
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Q5. What is Braodcast Join
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Broadcast Join is a technique used in distributed computing to optimize join operations.
• Broadcast Join is used when one table is small enough to fit in memory of all nodes in a cluster.

• The smaller table is broadcasted to all nodes in the cluster, reducing network traffic.

• Broadcast Join is faster than other join techniques when used appropriately.

• Example: Joining a small reference table with a large fact table in a data
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