Visual Designer
Choose preparation tools
Choose tools by the result you need, connect them on the canvas and inspect each step as you build. A saved workflow preserves those preparation steps for the next run.
Start with a report workflow
| Task | Tool | What to configure |
|---|---|---|
| Read a saved dataset, table or view | Database Source | Select Dataset, Table or View, then choose the source. |
| Load a source file | File | Select the file and review its columns. |
| Keep the rows you need | Filter | Choose a column and condition, or use an advanced expression. |
| Calculate totals and summaries | Aggregate | Select Group By columns and summary actions such as Sum, Count or Average. |
| Order the result | Sort | Choose the sort columns and directions. |
| Check a result | Preview | Connect the branch you want to inspect. |
| Keep the prepared dataset | Output | Choose DataQloo Storage or an existing workflow dataset. |
Follow Build your first workflow for a complete revenue-report example.
Clean and shape data
These additional preparation tools are available with Premium. See workspace plans for access details.
| Task | Tools | Typical use |
|---|---|---|
| Choose and organize columns | Select, Dynamic Select, Auto Column | Keep relevant fields and configure column handling. |
| Standardize values | Data Cleansing, Data Cleanse Pro, Imputation | Clean text, handle nulls and fill missing values. |
| Calculate new values | Formula, Multi Column Formula, Multi Row Formula | Apply calculations to a column, across columns or using surrounding rows. |
| Handle duplicates | Unique | Choose key columns and keep unique rows or inspect duplicates. |
| Prepare text and dates | Text To Columns, RegEx, DateTime | Split fields, match patterns and transform date/time values. |
| Reshape a table | Transpose, Cross Tab, Make Columns | Change between row-oriented and column-oriented layouts. |
| Work with JSON | JSON Parse, JSON Build | Extract structured values or build JSON from columns. |
| Sample or summarize a sequence | Sample, Random % Sample, Running Total, Count Rows | Select records and calculate row-based summaries. |
Combine related datasets
Join Multiple combines two or more inputs using shared keys or row position. Configure the matching fields and desired output, then check the preview's row count and columns.
Union stacks inputs into one result. Align columns by name, position or manual mapping, depending on how the source tables are structured.
Find Replace matches a lookup dataset against a column to replace text or append lookup columns. Append Columns combines a source dataset's columns with every target row; review its row-count warning when the source contains multiple rows.
For similar records with spelling differences, Fuzzy Match provides weighted Levenshtein, trigram and Soundex matching. Its Properties panel checks the required PostgreSQL extensions: pg_trgm for trigram matching and fuzzystrmatch for Levenshtein or Soundex. Inspect the scores and matched groups before using the result downstream.
Verify the transformation
Select each configured tool to inspect its preview. Check Schema and Profile when a transformation changes column types or value quality, and use Messages to resolve configuration issues.
When the branch is ready, select Run for the complete result. Review Run Details, then save or download the result.