Data discrepancies can surface as missing records, incorrect values, or fields not being correctly typed. If something in your data warehouse doesn’t look quite right, these resources will help you get to the root of the problem.
Places to Start
Pinpointing the cause of a data discrepancy has the potential to require quite a bit of investigation. To increase efficiency, we recommend using these three resources to perform quick checks on some of the more obvious and common causes.
If you’ve noticed missing or incorrect data in your data warehouse, this guide is the place to start. In it we’ll walk you through the most common causes of data discrepancies, how to verify the root cause, and how to fix it. We also outline the next steps should you need to contact support.
Occasionally, some integrations used by Stitch may encounter bugs or other issues. Whenever we’ve identified a third-party issue - meaning on the integration provider’s end - we’ll post an update here.
From time to time, some of the applications and databases we integrate with may experience downtime. During these outages, Stitch may be unable to successfully connect to your data source and replicate your data.
Additional & Integration-Specific Resources
If you’ve noticed some missing columns or data from your data warehouse, the root cause may be
Missing some Mongo data? The root cause may be multiple data types in the Replication Key field and how Mongo sorts data based on data type.
If you’ve noticed some missing data from your Segment integration, the culprit might be the selective integration snippet on your website.
If you don’t see all the fields you expect to in the Replication Key field for you Mongo integration, the root cause may be insufficient permissions or a lack of field indexing.
If you’ve noticed that some MySQL
TINYINT(1) columns are displaying as
BIT in Stitch, it’s usually due to how the MySQL driver converts this data type.
If a table isn’t replicating into your data warehouse, it may be because one or more of the columns in the table contains an unsupported data type.
When certain Postgres data types are replicated, they’ll be stored as
strings in your data warehouse.
If you’ve noticed some out-of-date Salesforce data in your data warehouse, the root cause may be a formula field.