Bigquery Declarative Sql Udf Analytics Processing

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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?

Extract all raw tables into Cloud Storage in JSON format, perform payload parsing in Dataproc Spark, and load the enriched data back into BigQuery

Deploy a BigQuery Remote Function backed by a Cloud Run HTTP service to parse payloads, select the latest event attributes, and return nested STRUCT and ARRAY values

Implement native GoogleSQL User-Defined Functions (UDFs) for parsing logic and use analytical aggregate functions like MAX_BY to extract the latest event attributes directly within the query

Use legacy SQL User-Defined Functions (UDFs) written in JavaScript to parse data and construct window partitions across the dataset

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Bigquery Declarative Sql Udf Analytics Processing

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