Our recent redesign simplifies the data transfer experience by combining raw and modelled data sources into a single object - data feeds.
If you see Data Feeds in the left navigation sidebar, go to this article.
These new features are being released gradually. If you don't see them in your account yet - no worries - they’re coming soon!
Are you seeing this error message when creating a metric?
This article explains why this message displays and what you can do about it.
Why am I seeing this error message?
You'll see this message if your modelled data source includes a LOT of rows. To respect fair use limits and enable optimum performance for all customers, we limit the number of rows of data that can be ingested per day, per account. This limit is applied during metric creation and when you choose to delete and re-import data for a metric. If you see this error message, it means you have exceeded your daily data ingestion limit.
What can I do to fit within the limit?
During metric creation and when you delete and re-import data for a metric, we strongly recommend you select a date column, instead of choosing the "Use the current date" option, especially for larger data sources. Learn more about creating custom metrics.
This is the step where you select a date column:
When you don't select a date column, we need to ingest the entire modelled data source when a metric is created (and on every refresh) which may cause you to hit the maximum data ingestion limit.
When you select a date column at metric creation, we group the rows in the modelled data source into daily buckets using the selected time dimension and count the number of rows in each daily bucket. The daily ingestion value for the custom metric is calculated using the single, highest row count of all the daily buckets, instead of a total of ALL the rows. This significantly reduces the data being ingested and makes it less likely you'll exceed the maximum limit.
To fit within the data ingestion limit, try:
- Reducing the number of rows in your modelled data source, for example, by applying filters when querying your data for custom metrics.
- Adding a date column to your data source if it doesn't have one.