Developing ETL pipelines in PySpark for handling complex data transformations

Start with a data contract: input schema and nested structure, expected record grain, keys, null handling, allowable duplicates and destination semantics. Preserve an immutable raw copy and record a run or source-batch identifier for replay.

Implement transformations as small DataFrame functions with explicit input/output schemas. Prefer built-in operations such as from_json, struct-field selection, array functions and explode_outer when they express the rules. Flattening an array changes row grain; reconcile record counts and totals after that step rather than treating multiplication as automatically correct. Quarantine malformed or incompatible records with a reason. Built-in functions

Filter and project early when semantics allow it. Select partitioning from actual joins, aggregations and output reads, then inspect the physical plan. Broadcast only a sufficiently small side that fits each executor's working budget; it is not a general mechanism for sharing large datasets cheaply. Use DataFrame aggregations for authoritative result totals. Accumulators can aid diagnostics, but updates inside transformations may be repeated when work is re-executed. Shared-variable behavior

Choose Parquet, ORC or a suitable table format for the required types and access patterns. Schema evolution must be deliberate: a format's ability to represent added columns does not establish semantic compatibility, atomic publication or safe concurrent writes. Parquet schema merging

Publish through a destination-specific atomic or idempotent protocol. Validate row counts, key uniqueness, referential integrity, rejected-row totals and business aggregates before exposing a new batch. Test empty arrays, null structs, corrupt inputs, duplicate delivery and a failure between writing and marking a batch complete. Keep enough lineage and metrics to reproduce a run without logging secrets or unrestricted raw payloads.