A city analytics team wants Spark and Kafka-like capacity without patching brokers or cluster nodes every week. Which cloud service range best matches that goal?
Select an answer to reveal the explanation.
Short Explanation
Picture renting a kitchen where the stove stays tuned for you, not buying the whole building. Managed Spark and Kafka-style PaaS keeps the engines available while the provider handles much of the patching and cluster babysitting. Bare VMs and pure dashboards sit on different parts of the service ladder.
Full Explanation
PaaS-style managed Big Data services expose streaming and compute platforms while the provider operates the underlying control plane, upgrades, and much of the cluster lifecycle. That pattern reduces municipal ops toil compared with IaaS virtual machines the city must still harden and patch. SaaS analytics consume prepared results rather than hosting the Spark or Kafka engines themselves. Selecting PaaS is the BDPC-aligned match when the requirement is managed Spark/Kafka-like capacity with less operational burden.