Real questions from top companies in Spark/Big Data · hard
How would you optimize Glue jobs to reduce processing time for large datasets?
How would you optimize Spark jobs for better performance?
How would you optimize a Spark job that takes too long to run in production?
How would you optimize a slow-running notebook in Databricks?
How would you optimize your Spark Streaming ETL pipeline for high throughput and low latency?
How would you read a large file (e.g., 15GB) efficiently in Spark by increasing parallelism?
How would you read data from an RDBMS using Spark? Provide the syntax.
If a consumer fails to process a message due to data corruption, describe how you would configure Kafka to handle retries and avoid message loss.
Implement a Kafka consumer that writes streaming data into a database.
Implement a PySpark job to read CSV data, perform joins, and store output as partitioned Parquet.
Importance of each layer in Databricks.
In Spark, what is the difference between cores and executors?
Justify the choice of your current tech stack. Why Spark, Hadoop, or cloud platforms?
Lambda, Kinesis, DynamoDB - data streaming and persistence
Limiting Parallel Tasks
List all the technologies you have worked on in your project (e.g., Spark, Hadoop, Hive, Databricks).
Load CSV from HDFS
Load data into Hive table from HDFS or local
Logical Plan workflow when submitting Spark queries?
Memory Management in Spark - executor, storage, shuffle memory
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