Big Data Engineer Lead Interview Questions and Answers

I used batch processing by breaking down large data sets into smaller chunks for easier processing.

• Implemented batch processing using tools like Apache Spark or Hadoop

• Chose batch processing for its ability to handle large volumes of data efficiently

• Split data into smaller batches to process sequentially for better resource management

I will use PySpark to handle 100 files of 100 GB size in an end-to-end pipeline.

• Use PySpark to distribute processing across a cluster of machines

• Read files in parallel using SparkContext and SparkSession

• Apply transformations and actions to process the data efficiently

• Utilize caching and persisting to optimize performance

• Implement fault tolerance and recovery mechanisms

• Use appropriate data storage solutions like HDFS or cloud storage

Code to print top two highest numbers from an array

• Sort the array in descending order

• Print the first two elements of the sorted array

Yes, it is possible to have a MapReduce job with 0 reducers.

• In some cases, the output of the map phase may not require any further processing or aggregation, so having 0 reducers is sufficient.

• For example, if the goal of the MapReduce job is simply to filter out certain data points based on a condition, there may be no need for a reducer.

• Having 0 reducers can also be useful for jobs where the output of the map phase is already in the desired format and does not need any addit...read more

Code in Spark for Big Data Engineer Lead interview

• Use SparkSession to create a Spark application

• Read data from a source like HDFS or S3

• Perform transformations and actions on the data using Spark RDDs or DataFrames

• Write the processed data back to a sink like HDFS or S3