Best Practices: Avoid the "Maximum Daily Analytics Dataflow Executions has been exceeded" error in CRM Analytics

Optimize Dataflows and Recipes to run under 2 minutes

• Dataflow and recipe runs under 2 minutes are excluded from this limit
   

Determine your data freshness requirements

• First, consider how often your users will actually need to see updated data for each dataset.
• Once you have identified how often your datasets need to be updated, schedule their associated dataflows to run only when needed.
• The same guidance applies to recipes as well. Only schedule the recipe to run as often as users actually need to see fresh data.

Consider having your hourly dataflows start and stop running at specific times

• For dataflows that are scheduled to run hourly, you can specify a time of day for them to start and stop queueing. For example, users might not need to see updated data after business hours.
• The start time determines when the dataflow will first run each day, and the stop time determines when the last dataflow run will begin.
• For more information about starting and stopping dataflows, see Schedule a Dataflow to Run Automatically.

Identify dataflows with common scheduling requirements and consider combining them

• If you have multiple dataflows that use a similar schedule, consider combining them into a single dataflow and then setting a schedule that meets the data freshness requirements for all included datasets.

Consider moving data manipulation tasks from recipes to dataflow nodes

• If you have multiple scheduled recipes performing data manipulation tasks on your datasets, these could quickly add up to use a significant portion of your daily dataflow runs.
• There may be situations where you could move data manipulation tasks from using a recipe to instead using transformations such as computeExpression, append, or augment directly in the dataflow.
• For more information about the various dataflow transformations, see Transformations for Analytics Dataflows.