spark-data-engineering

Build a production-ready Apache Spark data pipeline by enforcing explicit StructType schemas, composing pure DataFrame transformations, and applying adaptive query execution with shuffle and file-size tuning to prevent inefficient shuffles and schema drift.

Handle schema drift in PySpark Structured Streaming by enforcing schema-first ingestion with explicit StructType schemas and implementing checkpointing to ensure state recovery and prevent unexpected data quality issues during streaming processing.

Optimize broadcast joins for small tables in Spark ETL jobs by configuring adaptive query execution and applying shuffle partition tuning, which reduces unnecessary data exchange and improves overall DataFrame transformation performance.

Yes, you can use AWS Glue DynamicFrames with Apache Spark by leveraging Glue DynamicFrame-to-DataFrame integration, allowing you to apply pure DataFrame transformations and partitioned Parquet writes within your Glue jobs.

Partition Parquet outputs effectively in Spark batch processing by applying a strict partitioning strategy based on date or business keys, and configuring output file sizing controls to prevent the generation of unsafe or undersized files.

Spark data pipelines fail data quality checks when lacking testable quality gates, which you can fix by implementing schema validation, pure DataFrame transformation composition, and structured unit tests before writing to Delta or Parquet.