from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need

from clients that end-users create, and the engines are standalone applications with the full capabilities of Spark SQL, Flink SQL(under dev), running on single-node machines or clusters. The share level improve the overall resource utilization of the cluster, • At cluster layer, we leverage the capabilities, such as Capacity Scheduler, of resource scheduling management services, such as YARN and K8s use more resources more efficiently. On the one hand, we need to rely on the resource manager’s capabilities for efficient resource allocation, resource isolation, and sharing. On the other hand, we need