Issue-centric calculation for data products #

We evolved the display of issues to be failure-centric rather than asset-centric.

This improves our accuracy when tracking issues that impact data products.

For example, if 2 assets in your data product failed and each had 2 failures, we showed 2 issues.

Now, we add all the failures and give you the total number, in our example, 4.

Redshift query logs #

We can now fetch query logs from Redshift. What that means is we can identify cost optimization opportunities for our customers using Redshift.

We can for example:

• See which models run more often that they should
• Which models are not used downstream
• Which models are the most expensive