Data Pivoting
A practical ETL guide to transforming rows into columns for reports, dashboards, business intelligence, Excel summaries, and analytics.
Data pivoting transforms row-based data into a column-based structure. In ETL, pivoting is used when reporting, dashboards, Excel outputs, Power BI models, or executive summaries need values such as months, regions, metrics, warehouses, or questions shown as columns. It is rows-to-columns, not magic. Although after the third finance workbook of the day, it may feel close.
What Is Data Pivoting?
Data pivoting is a transformation that turns row values into columns. A source table may store one row per product, month, and amount. A pivoted output may store one row per product, with January, February, and March as separate columns. The values are the same. The shape is different.
In data transformation, pivoting is a presentation-oriented reshaping step. It usually happens after extraction, cleansing, filtering, mapping, sorting, and often after aggregation. The goal is not to create the best storage model. The goal is to create a shape that a report, dashboard, spreadsheet, or business process can read easily.
That distinction matters. Pivoting is not the same as data unpivoting. Unpivoting turns columns into rows, often to prepare spreadsheet reports for database loading. Pivoting turns rows into columns, often to prepare database or staging data for reporting. One tidies data for systems. The other presents data for people.
Pivoting is also not the same as data aggregation. Aggregation calculates totals, counts, averages, and other measures. Pivoting changes the layout. Many pivots use aggregated values, but the pivot itself is the rows-to-columns transformation.
A practical ETL pivot has three named parts: row fields, column fields, and value fields. Row fields stay down the left side of the output. Column fields provide the new column names. Value fields fill the cells. If those three parts are not clear, the pivot design is not ready.
For example, a product sales table may use product and region as row fields, month as the column field, and sales amount as the value field. A survey table may use respondent as the row field, question code as the column field, and answer as the value field. A finance table may use account and period as row fields, scenario as the column field, and amount as the value field.
That is why pivoting belongs late in many ETL workflows. Early stages should keep data flexible and traceable. Pivoting creates a fixed presentation shape. That shape is useful, but it is less flexible when the business asks a new question next month. And they will. It is one of the laws of reporting, somewhere between gravity and "someone renamed the worksheet."
Why Data Pivoting Matters
Data pivoting simplifies reporting. Business users often want one row per product, customer, branch, account, or department, with periods or metrics across columns. That shape is easier to scan than a long list of repeated rows. The database may prefer rows. The board pack often prefers columns. Both can be right, which is how ETL earns its tea.
Pivoting improves dashboard design when the target visual or model expects fixed measure columns. A KPI table with columns such as revenue, orders, returns, margin, and conversion rate is easier for many dashboard tools and business users to understand.
Business intelligence workflows often need pivoted datasets for specific outputs. Power BI can model normalized data very well, but some exports, custom visuals, legacy reports, and Excel outputs still need wide report-ready tables. Pivoting prepares those outputs without making the source database carry a presentation structure everywhere.
Pivoting can summarize transactional data when it follows aggregation. For example, transaction rows may first be grouped by product and month. The monthly totals can then be pivoted so each month becomes a separate column. The aggregation creates the monthly total. The pivot arranges those totals.
Readability matters too. A row-based dataset is efficient for systems, but a pivoted report can make comparisons obvious. Regional revenue side by side. Actual, budget, and forecast side by side. Warehouses side by side. That is the point: the pivot serves the person reading the output.
Excel and Power BI are common destinations. Advanced ETL Processor can create reports, update Excel files, and prepare database tables, so pivoting fits naturally near the end of a workflow. Use it when the final consumer needs columns, not when the raw detail should remain normalized.
Executive reporting often benefits from pivoted structures because leaders usually need comparisons, not raw records. Monthly actuals beside budget. Regions side by side. Warehouses beside each other. The report should answer the question quickly without asking the reader to mentally group 400 rows before their first coffee.
Pivoting also helps when the target format is fixed. Many Excel templates, legacy reporting systems, regulatory extracts, and departmental packs expect specific columns. The source system may store clean row-based facts, but the final output must match a column layout. ETL pivoting bridges that gap without forcing the storage model to become a report template.
The good news is that pivoting can be automated once the output contract is clear. The same workflow can read new rows, validate categories, calculate or summarize values, pivot the output, write an Excel file, and send it to the next process. That is much better than rebuilding a pivot table every Friday afternoon while pretending the weekend is still safe.
How Data Pivoting Works
A pivot needs four design choices. First, choose the row identifier fields. Second, choose the field whose values become columns. Third, choose the value field that fills the cells. Fourth, choose what happens when more than one source row maps to the same output cell.
Source rows Pivot rule Report columns ----------- ---------- -------------- Product + Month + Sales ---> Month becomes columns ---> Product + Jan + Feb + Mar Region + KPI + Value ---> KPI becomes columns ---> Region + Revenue + Orders Item + Warehouse + Qty ---> Warehouse becomes columns ---> Item + WH1 + WH2 + WH3
Start with monthly sales rows:
| Product | Month | Sales Amount |
|---|---|---|
| Printer | Jan | 1200 |
| Printer | Feb | 1350 |
| Printer | Mar | 1280 |
| Scanner | Jan | 700 |
| Scanner | Feb | 820 |
| Scanner | Mar | 790 |
After pivoting month values into columns, the report looks like this:
| Product | Jan | Feb | Mar |
|---|---|---|---|
| Printer | 1200 | 1350 | 1280 |
| Scanner | 700 | 820 | 790 |
The same process works for surveys. Source data may contain one answer per row:
| Respondent | Question | Answer |
|---|---|---|
| R001 | Q1 | Yes |
| R001 | Q2 | 4 |
| R001 | Q3 | No |
| Respondent | Q1 | Q2 | Q3 |
|---|---|---|---|
| R001 | Yes | 4 | No |
Financial reports often pivot scenario values:
| Account | Period | Scenario | Amount |
|---|---|---|---|
| Sales | 2026-07 | Actual | 84000 |
| Sales | 2026-07 | Budget | 80000 |
| Sales | 2026-07 | Forecast | 86000 |
| Account | Period | Actual | Budget | Forecast |
|---|---|---|---|---|
| Sales | 2026-07 | 84000 | 80000 | 86000 |
Attendance records use the same idea. A table with student, date, and attendance status rows can be pivoted so dates become columns for a fixed weekly or monthly attendance report. Product sales can pivot product category, month, or region depending on the report layout.
Product sales often need one more step before pivoting: aggregation. If the source has one row per order line, and the report needs one cell per product and month, the workflow must sum the order lines first. Otherwise two January rows for one product map to the same January cell. That is not a pivot problem. That is an unresolved measure problem wearing a nice shirt.
Attendance reports may not use numeric aggregation at all. If one student has one attendance status per day, the pivot value is simply the status. If the source contains two statuses for the same student and day, the workflow should reject or route the conflict. Taking MAX of `Present` and `Absent` because the database allows it would be technically creative and practically terrible.
Survey data is similar. If each respondent answers each question once, a pivot can create one row per respondent with one column per question. If the survey allows multiple answers per question, pivoting may need concatenation, count columns, or a separate response table. The pivot should reflect the questionnaire design, not flatten nuance into a tidy lie.
Common Data Pivoting Scenarios
Most ETL data pivoting scenarios follow the same pattern: start from a clean row-based dataset, decide what should become columns, then produce a report-ready structure.
Sales by month
Source layout: One row per product, branch, month, and sales amount.
Pivot operation: Turn month values such as January, February, and March into separate columns.
Resulting structure: One row per product and branch with monthly sales columns.
ETL example: A sales fact table becomes an Excel-ready monthly sales report.
Revenue by region
Source layout: One row per period, region, and revenue amount.
Pivot operation: Pivot region values into columns such as North, South, East, and West.
Resulting structure: One row per period with regional revenue columns.
ETL example: A finance dashboard reads prepared regional columns for executive reporting.
Inventory by warehouse
Source layout: One row per item, warehouse, and quantity.
Pivot operation: Pivot warehouse codes into quantity columns.
Resulting structure: One row per item with warehouse quantity columns.
ETL example: Stock teams see item availability across warehouse locations in one row.
Survey analysis
Source layout: One row per respondent, question, and answer or score.
Pivot operation: Pivot question codes into answer columns.
Resulting structure: One row per respondent with question answers side by side.
ETL example: A survey export is prepared for analytics or a management summary.
KPI dashboards
Source layout: One row per team, period, metric, and value.
Pivot operation: Pivot metric names into columns such as revenue, orders, returns, and margin.
Resulting structure: One row per team and period with KPI columns.
ETL example: Power BI receives a dashboard-ready KPI table.
Budget reporting
Source layout: One row per account, department, scenario, period, and amount.
Pivot operation: Pivot scenario or period values into columns.
Resulting structure: Budget, actual, and forecast appear beside each other for variance reporting.
ETL example: Finance users compare actuals against budget without rebuilding the report manually.
Healthcare reporting
Source layout: One row per clinic, day, measure, and value.
Pivot operation: Pivot measures such as appointments, cancellations, and waiting-list count.
Resulting structure: One row per clinic and day with operational measure columns.
ETL example: A healthcare BI report receives a wide daily activity table.
Manufacturing statistics
Source layout: One row per line, shift, metric, and value.
Pivot operation: Pivot metric values into output, scrap, downtime, and defect columns.
Resulting structure: One row per production line and shift for reporting.
ETL example: Operations teams compare line performance without scanning event-level rows.
Sales by month is the classic example because period values naturally become report columns. The source layout is usually normalized: product, branch, month, and amount. The pivot operation turns month into columns. The resulting structure gives report users one row per product or branch, with month values side by side. That is ideal for Excel exports and monthly sales packs.
Revenue by region works the same way, but the pivot field is region. This is useful when executives compare territory performance across one period. The ETL workflow should standardize region names before pivoting, otherwise `North`, `Northern`, and `North Region` become separate columns and the report starts developing opinions of its own.
Inventory by warehouse is useful when users need a quick stock matrix. A normalized inventory table is better for storage and replenishment logic. A pivoted warehouse report is better for people checking stock availability across locations. Use the normalized table as the truth. Use the pivoted table as the view.
Budget reporting often pivots scenarios rather than periods. Finance users commonly want actual, budget, forecast, and variance side by side. The workflow may aggregate account rows first, pivot scenario values into columns, then calculate variance columns. Keep the calculations documented because finance reports have excellent memories when the numbers change.
Manufacturing statistics often pivot metrics. A source table with line, shift, metric, and value can become one row per line and shift with output, scrap, downtime, and defect columns. This is easier for dashboard visuals, but the raw event or measurement rows should remain available for investigation.
Data Pivoting Techniques
Pivoting can happen in SQL, ETL tools, Python, spreadsheets, reporting tools, or prepared data marts. The right technique depends on data size, scheduling, target format, and who owns the report logic.
SQL PIVOT
Advantage: Clear when the source is staged in a database and the pivot columns are known.
Disadvantage: Database-specific syntax and awkward handling when pivot columns change often.
Conditional aggregation
Advantage: Portable SQL pattern using SUM, MAX, COUNT, and CASE expressions.
Disadvantage: Can become long and repetitive when many columns are required.
Spreadsheet pivot tables
Advantage: Excellent for ad hoc analysis, checking a sample, and discussing expected output with business users.
Disadvantage: Poor fit for governed recurring ETL because manual workbook logic is hard to audit.
ETL Pivot transformations
Advantage: Good for scheduled workflows, source files, logging, validation, and repeatable reporting outputs.
Disadvantage: Still needs clear grouping keys, pivot column rules, and aggregation choices.
Python Pandas pivot and pivot_table
Advantage: Flexible for scripted processing, tests, prototypes, and analytics pipelines.
Disadvantage: Needs code ownership, dependency management, memory planning, and deployment discipline.
Business rules
Advantage: Keeps pivot columns aligned with reporting definitions, finance periods, KPI names, and dashboard expectations.
Disadvantage: Rules change, so ownership and versioning matter.
Dynamic pivoting
Advantage: Useful when new months, regions, warehouses, or metrics appear automatically.
Disadvantage: Can produce unpredictable schemas that break downstream reports.
AI-assisted workflows
Advantage: Helpful for suggesting pivot layouts, describing source patterns, and drafting rules from messy report data.
Disadvantage: Needs review and validation. The robot may produce a beautiful report with exactly the wrong columns.
Useful references include Microsoft SQL PIVOT documentation, Pandas pivot_table documentation, and Power BI query overview.
The strongest opinion here is simple: use spreadsheet pivot tables to agree the output, not as the permanent production process. They are brilliant for discussion. They are less brilliant when the monthly report depends on one workbook hidden on someone’s desktop called `final_really_final_approved.xlsx`.
SQL PIVOT is useful when the source data is already staged in SQL Server or another database with pivot support. It keeps processing close to the data and can perform well with indexes and clear grouping keys. The tradeoff is maintainability. A query with 36 fixed month columns is understandable. A query with several dynamic pivot layers can become the sort of script people avoid touching.
Conditional aggregation is often more portable. A query such as `SUM(CASE WHEN month = 'Jan' THEN amount END)` is plain SQL and easy to adapt. It also makes the aggregation rule explicit. The downside is repetition. If the report needs 60 metric columns, the query becomes long enough to need a packed lunch.
ETL Pivot transformations are useful when the source is not already in a database, or when the output needs file handling, scheduling, logging, and validation around the pivot. This is common for Excel reports, CSV extracts, API data, and repeatable management packs. The pivot is only one step in the workflow: the full process still needs input checks, output checks, and failure handling.
Dynamic pivoting is tempting because it adapts automatically. Use it carefully. A dynamic report can add new columns when new categories appear, which is useful for exploration. It is risky when a downstream dashboard, Excel template, or database table expects fixed columns. Dynamic schemas are exciting. Production reporting should rarely be exciting.
AI-assisted workflows can help identify likely pivot fields in messy data or draft a first rule. Treat that as a starting point. The final decision must still come from the report requirement and sample output. AI is useful, but it has never had to explain a wrong board report to finance at 8:30 a.m.
Real ETL Examples
Building monthly sales reports
Sales transaction rows are grouped by product, region, and month, then pivoted so each month becomes a report column. The output is easy to export to Excel or load into a reporting table.
Creating management dashboards
Operational KPI rows are pivoted into one row per department and day with columns for revenue, orders, returns, average order value, and failed jobs.
Pivoting transaction data for Power BI
A prepared summary table is pivoted before loading Power BI when users need fixed measure columns for a particular dashboard page.
Preparing Excel summary reports
A normalized sales table is transformed into a wide Excel-friendly report with region columns, month columns, or scenario columns.
Converting normalized data into reports
A fact table stores metric rows in a clean database structure. The ETL workflow pivots those rows only for report output, not for long-term storage.
Producing financial summaries
Budget, forecast, and actual values are pivoted into columns so finance teams can calculate variance and variance percentage directly.
Generating cross-tab reports
Rows containing branch, product category, period, and amount are pivoted into a table where categories or periods become columns.
Preparing attendance reports
Attendance rows by student and date are pivoted into date columns for a school or training report that must match a fixed template.
Related implementation material includes Data Unpivoting, Data Aggregation, Data Sorting, Data Filtering, SQL to Excel export automation, Excel templates, Transformer tutorial, and the Transformation hub.
Power BI preparation example
| Department | Period | Metric | Value |
|---|---|---|---|
| Sales | 2026-07 | Revenue | 84000 |
| Sales | 2026-07 | Orders | 1260 |
| Sales | 2026-07 | Returns | 42 |
| Department | Period | Revenue | Orders | Returns |
|---|---|---|---|---|
| Sales | 2026-07 | 84000 | 1260 | 42 |
Inventory report example
| Item | Warehouse | Quantity |
|---|---|---|
| P-100 | WH1 | 18 |
| P-100 | WH2 | 7 |
| P-100 | WH3 | 0 |
| Item | WH1 | WH2 | WH3 |
|---|---|---|---|
| P-100 | 18 | 7 | 0 |
These examples show the same rule in different clothes. Keep stable identifiers as rows. Convert category values into columns. Put the measure values into cells. Validate totals after the pivot so the pretty report still matches the source.
Common Data Pivoting Challenges
Dynamic columns can break fixed reports
Dynamic columns are the first challenge. New months, regions, warehouses, metrics, or question codes may appear in the source. That is useful for discovery but risky for reports with fixed schemas. A dashboard expecting `Jan`, `Feb`, and `Mar` may not enjoy receiving `Apr` unannounced. Dashboards are sensitive creatures.
Missing categories need deliberate handling
Missing categories need deliberate handling. If a product has no March sales, should the March column show blank, null, or zero? Those are not the same thing. Zero means something happened and the amount was zero. Null may mean no source row existed. Blank may mean the report template wants visual quiet.
Duplicate values make cells ambiguous
Duplicate values cause ambiguity. If two source rows map to the same output cell, the workflow must know whether to sum them, count them, keep the latest, take the maximum, or reject the conflict. This is where pivoting often depends on aggregation and deduplication before layout changes.
Aggregation choices must be explicit
Aggregation choices must be explicit. Sales amounts may use SUM. Status values may use MAX only if the values are coded safely. Survey answers may need one response per respondent and question. If there are two answers, the real problem is not the pivot. It is the source rule.
Large datasets can be expensive to pivot
Large datasets can become expensive to pivot. Wide outputs need memory, sorting, grouping, and sometimes dynamic SQL. Pivot close to the reporting layer when possible, and keep detailed normalized rows available for drill-through and audit.
Changing source values can create wrong columns
Changing source data can break column rules. If region names change from `North` to `Northern`, the pivot may create a new column instead of filling the expected one. Use standardization and lookup rules before pivoting when column names must be stable.
Column ordering should follow business order
Column ordering is another small problem that becomes visible quickly. Alphabetical order puts `Apr` before `Feb`, which is technically sorted and practically daft. Month, quarter, scenario, and KPI columns should use business order. Store a sort key in reference data rather than relying on column names.
Mixed metric types may need separate outputs
Mixed metric types can cause awkward outputs. A generic KPI pivot may put currency, counts, percentages, and text statuses into neighbouring columns. That may be fine for a report. It may be poor for a typed database target. When types matter, split outputs by metric family or define explicit typed columns.
Pivoting can hide source detail
Finally, pivoting can hide detail. Once rows become columns, it is harder to see which source rows contributed to each value. Keep source keys, batch IDs, and summary logs. If a number looks wrong, the report should lead back to the evidence instead of shrugging in spreadsheet.
Best Practices for ETL Data Pivoting
- Define the purpose of the pivot before choosing columns.
- Keep normalized detail rows unchanged for audit and drill-through.
- Aggregate or deduplicate source rows before pivoting when duplicate cell values are possible.
- Use stable pivot column names when reports, exports, or dashboards depend on them.
- Document grouping fields, pivot field, value field, and aggregation rule.
- Handle missing categories deliberately with nulls, zeroes, or blank output cells.
- Validate row counts, totals, and control values before and after pivoting.
- Avoid dynamic pivots when downstream systems require a fixed schema.
- Sort output columns in a business-friendly order, not alphabetically by accident.
- Test with new months, missing regions, duplicate rows, null values, and unexpected categories.
- Keep pivot logic close to the ETL workflow that creates the report.
- Do not use pivoted layouts as the only storage format unless the target requires it.
The practical rule is this: pivot for consumption, not storage. Keep the row-based source or summary table when users need audit, filtering, drill-through, or later modelling. Create the pivoted layout when the report, Excel output, or dashboard specifically needs columns.
Use a sample output before building the full workflow. A small before-and-after table catches more misunderstandings than a long requirements paragraph. If the business user can approve the sample, the ETL developer can build the repeatable version without guessing. Guessing belongs in pub quizzes, not finance reports.
Validate after pivoting. Compare source totals with pivot totals. Count source groups and output rows. Check that expected columns exist. Check that unexpected columns are handled. If a new category appears, the workflow should log it clearly instead of quietly producing a report no one expected.
Keep the pivot rule versioned. Reports change because businesses change. New regions appear. Finance adds scenarios. Operations adds KPIs. If a column was added, renamed, or removed, record why. Six months later, that note may save someone a long afternoon of report archaeology.
Data Pivoting vs Data Unpivoting
| Question | Data Pivoting | Data Unpivoting |
|---|---|---|
| Direction | Turns rows into columns. | Turns columns into rows. |
| Typical purpose | Create report-ready, dashboard-ready, or Excel-friendly output. | Prepare wide spreadsheet reports for database loading and analysis. |
| Example | Month rows become `Jan`, `Feb`, and `Mar` columns. | `Jan`, `Feb`, and `Mar` columns become month rows. |
| Related guide | This data pivoting guide. | Data Unpivoting. |
Data Pivoting vs Data Aggregation
| Question | Data Pivoting | Data Aggregation |
|---|---|---|
| Main purpose | Change row values into columns. | Calculate grouped totals, counts, averages, or other measures. |
| Example | Show each month as a separate column. | Sum sales by product and month. |
| Relationship | Often presents aggregated values in a wide layout. | Often prepares values before they are pivoted. |
| Related guide | This pivoting guide. | Data Aggregation. |
Data Pivoting vs Cross-Tab Reports
Pivoting is the transformation. A cross-tab report is one common result. The difference matters because an ETL workflow may pivot data before creating a cross-tab file, dashboard table, or Excel summary.
| Question | Data Pivoting | Cross-tab report |
|---|---|---|
| Main meaning | The ETL operation that turns selected row values into columns. | A report layout that displays categories across rows and columns. |
| Example | Pivot month values into month columns. | Show products down rows and months across columns. |
| Focus | Transformation rules, grouping fields, value field, and aggregation handling. | Presentation, readability, totals, formatting, and user consumption. |
| Best use | Prepare report-ready data inside an ETL workflow. | Present pivoted or summarized data to users. |
Checklist for Deciding When to Pivot Data in ETL
Use this checklist before adding a pivot transformation. It is less exciting than discovering a dashboard broke because a new region created a surprise column, which is precisely the point.
- Is the pivot needed for presentation, reporting, export, dashboarding, or analytics?
- What is the output grain: product, customer, branch, day, month, department, respondent, or another level?
- Which fields stay as row identifiers?
- Which field becomes column names?
- Which field provides the cell value?
- Which aggregation rule applies when multiple rows map to the same output cell?
- Should missing categories appear as blank, null, zero, or omitted columns?
- Are pivot columns fixed or dynamic?
- Does the target report, Excel template, or Power BI model require a fixed schema?
- How will row counts and totals be reconciled after pivoting?
- Should filtering, sorting, standardization, or aggregation happen before pivoting?
- How will new categories, renamed values, and changed business rules be handled?
For the wider transformation process, start with What is Data Transformation?. For the full topic collection, use the Transformation hub.
Frequently Asked Questions
What is data pivoting?
Data pivoting is the process of transforming row-based data into a column-based structure. In ETL, it is commonly used to prepare reports, dashboards, Excel outputs, Power BI models, and business summaries.
What is data pivoting in ETL?
Data pivoting in ETL turns values from one field into output columns. The workflow keeps grouping fields as rows, chooses a pivot field for column names, selects a value field, and applies an aggregation rule when needed.
What is a simple data pivot example?
A sales table with product, month, and amount rows can be pivoted so January, February, and March become columns. The output has one row per product and one column per month.
Is data pivoting the same as data unpivoting?
No. Data pivoting turns rows into columns for presentation and reporting. Data unpivoting turns columns into rows for storage, normalization, and easier ETL processing.
Is data pivoting the same as data aggregation?
No. Aggregation calculates totals, counts, averages, or other measures. Pivoting changes the layout by turning row values into columns. Many pivots use aggregated values, but the actions are different.
Is a pivot the same as a cross-tab report?
Not exactly. Pivoting is the transformation that creates a wide structure. A cross-tab report is a common output format produced from pivoted or grouped data.
When should data be pivoted in ETL?
Pivot data near the reporting or export stage when the target needs columns for months, regions, metrics, scenarios, warehouses, or questions. Keep detailed normalized rows if audit, drill-through, or later modelling is needed.
What are common data pivoting techniques?
Common techniques include SQL PIVOT, conditional aggregation, spreadsheet pivot tables, ETL Pivot transformations, Pandas pivot or pivot_table, business rules, dynamic pivoting, and AI-assisted workflows.
How do duplicate rows affect pivoting?
If multiple source rows map to the same output cell, the workflow must aggregate, choose one value, or reject the conflict. Without a rule, duplicate values make the pivot ambiguous.
How should missing pivot columns be handled?
Missing categories should be handled according to the reporting requirement. Some outputs need blank cells, some need zeroes, and some need fixed columns even when no source rows exist.
Can SQL pivot data?
Yes. SQL can pivot data with PIVOT where supported or with conditional aggregation using CASE expressions and aggregate functions.
Can Python pivot data?
Yes. Pandas provides pivot and pivot_table functions. Production workflows still need validation, logging, scheduling, and memory planning.
Does Advanced ETL Processor automate data pivoting?
Yes. Advanced ETL Processor automates data pivoting with built-in Pivot transformations, SQL, expressions, Python scripts, workflow automation, and AI workflows.
Automate Data Pivoting in Advanced ETL Processor
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