myrtle [email protected]

This chapter also discusses scaling and troubleshooting. Scaling is one benefit (perhaps the most helpful) of running data analytics on the Azure platform. If your data procedures are allocated a specific amount of compute power but need more, they get more. If your application works fine when you have 100 files but you unexpectantly receive 1000, you can add additional compute capacity, in real time, to handle the additional workload, by scaling out or up. Scaling out means adding more identical nodes to the worker pool, and scaling up means increasing the number of CPUs and memory allocated to a single node.