PySpark (Apache Spark) Developer Test

This skill tests a developer’s ability to work with low-level Spark RDDs — the foundational abstraction in Spark for fault-tolerant, distributed data processing. Questions focus on parallelizing collections, partitioning, and applying core transformations like map, flatMap, reduceByKey, and groupByKey. Understanding RDDs is critical for scenarios requiring fine-grained control over execution and when working with unstructured or semi-structured data. RDD-based programming also deepens understanding of shuffles, narrow vs wide dependencies, and execution plans.

This skill assesses proficiency in working with Spark’s structured data APIs — primarily DataFrames — and the ability to integrate with external data sources. It includes schema inference, explicit schema definition using StructType, column-level operations, and reading/writing data in formats like CSV, JSON, and Parquet. Efficient use of DataFrames is crucial for writing performant Spark applications, especially those relying on Spark SQL or Catalyst optimization. This skill also reflects a developer’s ability to bridge data engineering and analytics workflows.

This skill assesses a developer’s ability to implement real-time data processing pipelines using Spark Structured Streaming. Candidates are evaluated on their understanding of streaming sources and sinks, event-time vs. processing-time semantics, watermarks, output modes (append, update, complete), and checkpointing. Mastery of Structured Streaming is critical for building robust streaming applications that maintain state, handle late data, and scale efficiently. It also reflects readiness for production-grade streaming systems that ingest data from Kafka, socket sources, or file streams.

This skill covers diagnosing and resolving Spark application failures and runtime errors. Questions should focus on interpreting error messages, identifying root causes (e.g., OOM, serialization errors, schema mismatches, SparkContext shutdown), and selecting corrective actions. Avoid generating performance tuning or configuration optimization questions; focus strictly on troubleshooting and debugging scenarios.

This skill covers configuring and deploying Spark applications using spark-submit and runtime configuration parameters. It includes executor and driver memory settings, shuffle partition configuration, cluster mode selection (YARN/local), dynamic allocation, and resource tuning at deployment time. Avoid generating performance diagnosis or debugging questions; focus only on configuration-level control and deployment decisions.

This skill evaluates a developer’s use of User Defined Functions (UDFs) in Spark and the performance implications associated with them. It includes creating UDFs in Python/Scala, registering them with SparkSession, and understanding how UDFs bypass Catalyst optimization. Candidates are also assessed on alternatives like using built-in functions, expr(), or SQL expressions when possible. Mastery of this area is important to write performant and scalable Spark code that doesn’t degrade execution plans or cause serialization issues.

This skill covers writing and executing SQL queries in Apache Spark using Spark SQL. It includes registering DataFrames as temporary views, executing queries using spark.sql(), performing aggregations, joins, filtering, grouping, window functions, and using SQL syntax such as SELECT, WHERE, GROUP BY, HAVING, and CASE statements. Avoid generating performance tuning, runtime configuration, debugging, or DataFrame API chaining questions. Focus strictly on SQL query construction and Spark SQL usage.