Real questions from top companies in Spark/Big Data · hard
Explain how Glue's Spark-based architecture handles data parallelism.
Explain how HDFS (Hadoop Distributed File System) stores data across nodes.
Explain how I handle performance optimizations, scheduling tasks, and monitoring DAGs in Airflow.
Explain how Kafka handles real-time data streaming and guarantees message delivery.
Explain how Spark groups transformations into stages. What causes a stage boundary?
Explain how Spark handles data partitioning and the role of shuffles in performance tuning.
Explain how Spark processes a 500GB file, covering memory allocation, shuffles, and spillovers to disk.
Explain how spark.read.format("delta").load() works
Explain how to overwrite a file stored in S3 using PySpark.
Explain how to schedule an automated task using Apache Airflow.
Explain how you would design a partition strategy for a large dataset in HDFS.
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.
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