Performance is another very important aspect for solutions based on Data Lake store. Since Data Lake store is a repository, it should be storage intensive providing high throughput in terms of higher IOPS for reads and writes. Traditional storages does not consume the entire throughput available from the storage and hardware. Instead, Azure Data Lake store provides higher throughput by parallelizing the access to data and consuming the bandwidth available from hardware.

It is to be noted that for end-to-end solutions, it is just not the performance of Azure Data Lake store that is important, in fact the performance of all data sources from where data is ingested into Azure Data Lake store is also important along with services that accesses data for analytical purposes.