Using data engineering, you can explore, visualize, clean, and prepare data. The data engineering process is a common first step for many spatial analysis and mapping workflows. The Data Engineering view and ribbon can help you better understand the data and prepare it for GIS workflows.
You can do the following in the Data Engineering view:
- Open a Data Engineering view for one or multiple layers.
- Explore fields in the data by viewing a list of fields by type and quickly mapping and charting the data to understand patterns.
- Interact with statistics of the data to gain a better understanding of the values and distribution of the data.
- Prepare the data by applying geoprocessing tools to clean, construct, integrate, and format it.
Get started with a quick tour of data engineering
Example
For example, you have an educational attainment dataset for United States counties. When you open the Data Engineering view using this dataset, you can explore and prepare the data. The fields panel shows the field names or aliases and types, with buttons that symbolize and create a chart of a field, or you can go to the field in the attribute table. In the statistics panel, you can choose a subset of fields to show data quality metrics, show statistics, and preview charts. You can filter the results by data type to further explore the results or export the statistics to a stand–alone table.
Once you've investigated the data, you can use the tools and features on the ribbon to prepare the data. For example, you can use the Transform Field tool from the Construct gallery to normalize the fields, or you can use the Fill Missing Values tool from the Clean gallery to replace the null values. Alternatively, you can right-click the cell containing the number of nulls to open the Fill Missing Values tool. You can also use tools from the context menus of the statistics and fields panels in the Data Engineering view to fix issues in the data.