Power-BI-Dataset-Scale-Out-Read-Write-Path

Microsoft has this week announced a preview of its Power BI Dataset Scale-Out. This is a new dataset that allows enterprise users to access large-scale Power BI tools without needing any extra admin overhead of infrastructure.

With Power BI Dataset Scale-Out it is possible to scale dataset replicas and load-balance client connections to meet processing demands when needed. During times of low demand, Power BI will automatically scale back the replicas.

Microsoft points out this automated scaling allows customers to manage their peak workloads and not need to worry about losing value during non-peak times. Furthermore, the new scale-out solution provides better data refreshing thanks to its refresh isolation ability. It can separate read/write replicas of the dataset and refresh automatically.

Power BI enterprise customers can also leverage advanced data refresh optimization scenarios through the Enhanced Refresh REST API and TMSL Refresh.

Power BI Dataset Scale-Out Abilities

  1. “Cost-efficient dataset scalability: Enterprise customers can meet even the most critical demand during peak hours at no extra costs up to the maximum available compute resources of their underlying Premium capacities. For example, Power BI can scale out a 20 GB dataset on a P1/A4 capacity to utilize the eight available vCores of the P1/A4 capacity as best as possible. The individual dataset replicas do not count against the max memory limitation per dataset and customers are not charged for the additional memory consumption of the dataset replicas.
  2. Reliable performance for both refresh and query operations: Power BI maintains one read-write replica and additional read replicas and automatically synchronizes these replicas after update and refresh operations, by default. All refresh operations are performed on the read-write replica without impacting the read replicas that Power BI reports and other client applications use. This isolation ensures that refresh operations do not impact query processing and vice versa and delivers in this way maximum performance for both refresh and query operations.
  3. Maximized dataset memory utilization: When selecting a Premium SKU, the amount of memory required to refresh a dataset must be accounted for in addition to the target dataset size in memory. This typically limits the dataset size to approximately 50% of the max. available memory per dataset. By lowering the total memory requirements during processing through advanced refresh scenarios enabled via Premium Scale-Out, as mentioned earlier, less memory must be reserved for refresh operations allowing for larger datasets to be hosted on the selected Premium SKU. To find out how much memory is available for each dataset on a Premium capacity, refer to the Capacities and SKUs in the product documentation.”

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