Is Hadoop dead? Should we migrate off it?
On-prem Hadoop is in managed decline: talent is scarce and cloud object storage beats HDFS economics. But a working cluster is not an emergency. We typically migrate workload-by-workload to Spark-on-cloud (Databricks, EMR, Dataproc), retiring the cluster only after parallel-run validation.
How do you tune slow Spark jobs?
Profile first — skew, shuffle volume, partition counts, serialization. Common wins: AQE enablement, broadcast-join thresholds, salting skewed keys, right-sizing executors, and caching strategy. We routinely take multi-hour jobs to minutes without hardware changes.
Can Kafka really handle our peak volumes?
Properly partitioned Kafka handles millions of events per second. The real design work is partition-key choice, consumer-group scaling, schema governance, and back-pressure strategy. We design for your peak, then load-test to prove it before go-live.
What is dynamic data masking and why at 12M records/minute?
Masking replaces sensitive values (PAN, PII) with realistic substitutes as data moves between environments. Throughput matters because masking sits inside nightly windows — our engine sustains 12M+ records/minute so compliance never delays delivery.
Spark vs Flink — when do you choose which?
Spark (Structured Streaming) for unified batch+stream teams and micro-batch latencies of seconds; Flink for true event-at-a-time processing, large keyed state, and millisecond latency. We run both in production and choose per use case, not by fashion.