spark-engineer

Design scalable Spark ETL pipelines using DataFrame APIs with explicit schemas and thoughtful partitioning strategies. This approach ensures predictable latency and resource usage when ingesting, transforming, and writing partitioned outputs.

Tune Spark SQL performance by applying adaptive query execution (AQE), caching discipline, and shuffle optimization across batch and streaming workloads. This structured approach minimizes errors and ensures predictable resource usage during data processing.

Yes, PySpark DataFrames support streaming analytics and interactive queries across big data clusters. By applying explicit schemas and partitioning strategies, you can maintain scalable and efficient processing for both workload types.

Build a production-grade ETL by loading Parquet data, applying transformations and aggregations, and writing partitioned Parquet outputs. Using explicit schemas and partitioning ensures predictable latency and reliable execution.

Unpredictable Spark job latency often stems from poor partitioning strategies, lack of caching discipline, or unoptimized shuffles. Enforcing adaptive query execution and explicit schemas helps ensure reliable and efficient runs.

Use adaptive query execution (AQE) in Spark jobs to dynamically optimize shuffle partitions and query plans during runtime. Applying AQE alongside caching discipline ensures reliable performance across large-scale batch and streaming workloads.