Real questions from top companies · hard
Explain how you would implement real-time analytics using a streaming platform like Kafka or Kinesis.
Explain how you would use Kafka Connect to ingest data from a relational database into Kafka while ensuring minimal latency and exactly-once semantics.
Explain job execution in Spark: stages, tasks, Catalyst Optimizer
Explain read and write modes in Spark.
Explain repartition vs. coalesce. Which one would you use to reduce shuffle operations?
Explain the DAG in Spark and how it plays a role in execution.
Explain the Medallion architecture and its benefits in data engineering.
Explain the architecture and role of the Hive Metastore in a data pipeline
Explain the architecture of Databricks, including the control plane and data plane.
Explain the architecture of Kafka
Explain the architecture of Kafka and its core components.
Explain the architecture of Spark Streaming
Explain the architecture of Spark, including its components such as driver, executor, and cluster manager.
Explain the architecture of Spark, including the roles of driver, executors, DAGs, and SparkContext.
Explain the benefits of auto-scaling policies in EMR.
Explain the benefits of using columnar storage formats like Parquet or ORC.
Explain the concept of RDD, DataFrame, and Dataset in PySpark.
Explain the concept of consumer groups in Kafka. How do they affect message processing?
Explain the concept of preemptible VMs in Dataproc and their cost implications.
Explain the configuration of a Spark cluster for optimal performance
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