A public-works ETL that once ran as classic MapReduce is being rewritten as Spark batch jobs for speed and richer APIs. What shift does this represent?
Select an answer to reveal the explanation.
Short Explanation
Same ETL mission, newer bus: Spark batch often takes over jobs that used to ride classic MapReduce, thanks to speed and friendlier APIs. Handwritten ledgers and Bloom-filter-only ETL are not that upgrade path.
Full Explanation
Organizations commonly migrate batch ETL from classic MapReduce to Spark for performance and developer APIs while remaining in the Big Data ecosystem. Banning distributed compute, using only Bloom filters as engines, or requiring unrelated specialty credentials are not the described shift.