Import Mappers
How to increase data consistency and save time
Mappers is one of the key feature of EverReal data importer that allows you to customize how your data is processed and transformed before loading it to EverReal. Mappers can help you solve data compatibility issues, improve data quality, and save time and manual work.
Mappers work by applying rules to your data fields based on the conditions and actions you specify. For example, you can use a mapper to change the format of a number field, replace a missing value with a default one, or convert a text field using grok. This will greatly improve your data is consistent and accurate.
To use mappers, navigate to your integration detail page and select the tab mappers. Here you could see sections for 4 different entity, Owner, Property, Units and Tenants. In each section you could see the csv headers in the list and clicking on edits bring a set of operations that you can process, Here you can add functions for pre-processing value replacement etc. Following is an example mapper UI
Once you have a mapping template saved, you can use it to import your data source to EverReal. The EverReal data importer will apply the mappers and their rules to your data fields and transform them accordingly. You could also download the results of the mappers from the history tab to increase efficiency, and make any adjustments if needed.
Mappers are a powerful tool that can help you streamline your data import process and ensure that your data is clean and compatible with EverReal. With mappers, you can focus on your business goals and not worry about your data issues.
Defaulting Rule
Defaulting in mapping is a useful feature that allows you to specify a default value for a field in case the value is missing from the source data. For example, if you are mapping a property type is missing in your EPR, you can use defaulting to assign a generic name like "RESIDENTIAL" to those rows. To use defaulting in mapping, you need to add the default value in the default value section of the mapping configuration.
You can enter any value that is compatible with the target field type, such as a string, a number, or a date. Defaulting in mapping helps you avoid errors and gaps in your data transformation process.
Grok Replacement
Grok replacer is a tool that allows you to replace values in your data that follow a certain pattern. For example, if you have a field that contains the floor number of a building, and you want to convert it from a format like 0.OG to a numerical format like 0, you can use grok replacer to extract the number from the original value and replace it with the new value. This can help you simplify your data processing and avoid repeating the same replacement for multiple values.
To use grok replacer, you need to specify the pattern of the value you want to replace, and the format of the new value you want to generate. You can use regular expressions or predefined grok patterns to match the value. You can also use variables to capture parts of the value and use them in the new value. For example, if your original value is 0.OG and you want to replace it with 0, you can use the following grok pattern:
%{NUMBER:floor}.%{WORD:og}
This pattern will match any value that has a number followed by a dot and a word, and assign them to the variables floor and og. Then, you can use the following format for the new value:
floor
This format will use the value of the floor variable as the new value. So, if your original value is 0.OG, the new value will be 0. You can apply this grok replacer to any field that has a similar pattern in your data.
Value Replacement
Value replacement is a technique that allows you to modify your text by replacing a value of it with different values. For example, you can use value replacement to change the Wohnung to Appartment.
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