Big Data Engineer Lead
Big Data Engineer Lead Interview Questions and Answers
Q1. how did you do batch processing. why did you choose that technique
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
Q2. how will you handle 100 files of 100 GB size files in pyspark. Design end to end pipleline.
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
Q3. Code to print top two highest numbers from an array
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
Q4. Is passible map-reduce have 0 reducer ?
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
Q5. Optimization techniques
Optimization techniques are methods used to improve the performance of algorithms and systems.
Use parallel processing to speed up computations
Implement caching to reduce data retrieval time
Optimize data storage by using efficient data structures
Utilize indexing to quickly locate specific data
Fine-tune algorithms for better performance
Q6. Write code in spark
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
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