SCHEDULE

In the Ultra Transcenders books

DBX-DEP

Each book explains SCHEDULE in context, with comparison tables and the common traps.

Terms in this definition

• ALL

  A DAX function that ignores any filters and gives back every row of a table or every value of the named columns. Used within CALCULATE, it works as a modifier that clears filters, although REMOVEFILTERS states that intent more clearly where it is available.

• Set

  Secret permission in Key Vault for writing secrets; some older material refers to it as Create.

• Materialized view

  A view that stores its query output ahead of time. A serverless pipeline maintains it, refreshing it on demand with REFRESH MATERIALIZED VIEW, on a schedule or when upstream data changes, incrementally when it can.

• Streaming table

  Delta table built for incremental loading, declared with CREATE STREAMING TABLE (Databricks SQL or Lakeflow pipelines). Wrapping sources in STREAM, as in STREAM read_files(...), reads them as streams.

Related terms

• acr purge

  A container command, currently in preview, that runs as an ACR task either on demand or on a schedule. It removes tags matching a repository and tag regex once they pass a given age, can also clear untagged manifests, and anything it deletes is gone for good.

• Alert processing rules

  Rules applied after an alert fires that attach or suppress action groups, on a schedule if required. Only notifications are affected; the alert rule stays in place and keeps firing.

• Autoscale

  Azure Monitor capability that adds or removes instances on a schedule or once a metric rule has been true for its full duration. The maximum instance count merely sets a ceiling and never triggers scaling itself.

• Autoscale scaling plan

  Feature built into Azure Virtual Desktop that powers session hosts on and off and scales them according to a schedule.

• Azure Data Share

  Azure service for sending data to another organisation's subscription as full or incremental snapshots on a schedule.

• Azure Functions trigger

  Every function is started by exactly one thing. That thing might be a web call over HTTP, a schedule, the arrival of a blob, a message landing in a queue or a change in a database.

• Azure Machine Learning pipeline

  Reusable job that links components together, for example data preparation then training then scoring, and can be run on a schedule.

• Change detection

  With this kind of automatic page refresh, Power BI polls a single measure on a schedule and redraws the page only if the measure's result has changed. Only one such measure is allowed per model, and in the service it requires Fabric, Premium or PPU capacity; shared capacity cannot use it.