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Spark handles batch ETL, streaming, ML pipelines, and SQL analytics in one framework — which is why it shows up everywhere from Databricks lakehouses to Hadoop migrations. Performance is unforgiving though. Executor sizing, shuffle tuning, and partition strategy can be the difference between a job that finishes in minutes and one that takes down the cluster. Our Apache Spark consulting helps teams tune workloads and cut infrastructure spend.