Best practices when working with cloud databases

Cloud databases, like Snowflake and Google BigQuery, are intended to function as big data stores. Therefore, connectors to these databases in ArcGIS Insights have been designed to optimize performance with big data. Advanced string matching logic that is available for other connectors is not used for Snowflake or BigQuery. If you are using these databases, it is recommended that you use data cleaning techniques before using the data in Insights. Use the following guidelines when cleaning your data:

• Do not use null values and empty strings interchangeably. While null values and empty strings may appear as equivalent in the database, they are not treated as the same data input. Therefore, missing data values should be entered into the database consistently as either null values or empty strings, not both.
• Do not use strings containing spaces as null values or empty strings. Spaces are displayed as <No Data> in the data table but treated as characters in field calculations and advanced filters, which will affect your calculation and filter results. Empty strings should be trimmed from your data before using the data in Insights.
• String values should be entered using consistent spelling and casing. String values that are misspelled or use alternate casing will be treated as different values. You can check your data for entry issues using a visualization that aggregates categorical values, such as a bar chart. If two strings appear identical but are still being treated as separate categorical values, there may be additional empty spaces either before or after one or more of the strings.