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A data engineering team is designing a high-throughput batch transformation pipeline in BigQuery to process billions of customer interaction records daily. The pipeline needs to parse complex semi-structured payloads, determine the customer's most recent interaction event attributes per session without complex joins, and return structured objects containing arrays of historical actions.
The solution must run natively at scale within BigQuery, maximize query execution performance, avoid external concurrency throttling, and support caching and complex types like ARRAY and STRUCT.
Which declarative processing approach should the team implement?
Native GoogleSQL User-Defined Functions (UDFs) allow developers to write reusable scalar and table-valued transformation logic in standard SQL. When combined with advanced analytical and aggregate functions such as MAX_BY (or ANY_VALUE(... HAVING MAX ...)), BigQuery can efficiently evaluate complex row-level transformations and time-based attribute selections in a distributed fashion.
StudyCerts
StudyCertsSTRUCT and repeated ARRAY fields.MAX_BY function retrieves the value of a target column corresponding to the maximum value of another column (such as event_timestamp) within an aggregation or window frame, eliminating the need for self-joins or nested ranking subqueries.STRUCT, ARRAY, JSON, and native date/time data types.Native GoogleSQL UDFs combined with advanced aggregate functions execute entirely in-engine with native slot allocation, supporting full SQL type hierarchies (STRUCT, ARRAY) and deterministic query result caching.
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