Import from flat file
- Locate and remove or modify missing or mismatched data
- Unnest complex data structures
- Identify statistical outliers in your data for review and management
- Perform lookups from one dataset into another reference dataset
- Aggregate columnar data using a variety of aggregation functions
- Normalize column values for more consistent usage and statistical modeling
- Merge datasets with joins
- Append one dataset to another through union operations
Most of these operations can be executed with a few mouse clicks. This section provides a basic overview of common workflows through the
Before you begin, please verify the following:
- : You You have a a and can login.
- If you do not have an account, you may be able to self-register through the application.
- Example data: You You should use a sample set of data during this workflow.
- Review object overview: Before you begin, you should review the overview of the objects that are created and maintained in the webapp. See Object Overview.
- Import data: Integrate data from a variety of sources of data. See Import Basics.
- Profile your data: Before, during, and after you transform your data, you can use the visual profiling tools to quickly analyze and make decisions about your data. See Profiling Basics.
- Build transform recipes: Use the various views in the Transformer Page to build your transform recipes and preview the results on sampled data. See Transform Basics.
Generate Results: Launch a task to run your recipe on the full dataset. Review results and iterate as needed. See Generating Results Basics.
- Export results: Export the generated results data for use outside of . See Export Basics.
If you walked through this workflow in the , you have imported, cleansed, transformed, and possibly enhanced your data for use in the next step of your analytics pipeline. Hopefully, this process has given you insight into the easy-to-use tools at your disposal through the and how quickly they can be brought to use in turning imported datasets into clean and actionable data for use across the enterprise.