""Practical insights into the Data Engineer interview process for mid-level professionals." I recently attended an interview after working as a data engineer for about 4 years. The procedure put my knowledge of Hadoop and Spark technical details as well as my Scala coding abilities to the test. Here is a thorough, round-by-round account of my experience. Phase 1: Online Assessment Both theoretical and coding questions were covered in the first round of the online exam."
"Hadoop's resource management layer is called YARN (Yet Another Resource Negotiator). It controls how CPU and memory resources are distributed throughout the cluster. YARN increases the system's scalability and efficiency by separating resource management from job execution. Key components: Resource Manager (RM): Allocates cluster-wide resources. Node Manager (NM): Manages resources on each node. Application Master (AM): Manages execution of a single job."
The hiring process comprised two phases: an online assessment and a technical interview. The online assessment combined multiple-choice questions on Java, NoSQL, Hadoop, Spark, and Scala with two Scala programming problems targeting Scala fundamentals and problem-solving. The technical interview included coding exercises and conceptual questions, such as a Scala program to verify Fibonacci membership and explanations of YARN and Spark dynamic allocation. YARN functions as Hadoop's resource management layer, separating resource management from job execution and involving Resource Manager, Node Manager, and Application Master components. Spark dynamic allocation automatically scales executors using parameters like spark.dynamicAllocation.enabled, minExecutors, and maxExecutors.
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