Transform using SQL
Infoveave Data Automation — Language Transforms
Tabular data in. SQL query applied. Reshaped, analytics-ready output out.
Data engineers and analysts who know SQL can use Transform using SQL to express virtually any row-level or aggregate transformation — filter, group, join, derive, rank — without switching tools or writing custom code. Working on the output of any upstream activity as a virtual table, the activity makes complex transformations accessible through the SQL language that most data teams already know. This is especially valuable for ad-hoc reshaping, analytical aggregations, and building reusable transformation logic that can be version-controlled as a SQL statement.
What Transform using SQL does
Apply SQL SELECT, GROUP BY, HAVING, and JOIN queries to tabular data inside your Infoveave workflow using the Transform using SQL activity. Filter, aggregate, derive columns, and reshape datasets with standard SQL syntax — no database required.
When to use Transform using SQL
- You need to apply complex filtering, aggregation, or column derivations that are cleaner to express in SQL than with individual no-code activities
- You want to GROUP BY, SUM, COUNT, or compute window functions on upstream workflow data
- Your transformation logic is already written in SQL and you want to reuse it directly in the workflow
- You need to join two upstream tables using SQL JOIN syntax within a single workflow step
When to avoid it
- Your transformation is a simple column rename or type cast — use dedicated transformation activities which require no SQL knowledge
- You need to write back to a database rather than transform in-memory — use database write activities instead
- Your input table is empty — the activity requires at least one column and one row to process
Where it fits in your Infoveave automation
Transform using SQL is one step inside a multi-step Infoveave workflow. Chain it with other activities — no code, no manual hand-offs.
Build this workflow visually in Infoveave Data Automation — drag, connect, and schedule with no infrastructure setup.
How teams use Transform using SQL
Real scenarios where this transformation saves hours of manual work.
Account Reconciliation Aggregation
Transaction data ingested from multiple files is aggregated using Transform using SQL — grouping by account code and summing debit and credit amounts. The single SQL step replaces a chain of aggregate-by-column activities, and the resulting reconciliation summary feeds the downstream comparison step.
Shift-Level OEE Computation
Machine sensor readings ingested from the shop floor are grouped by machine ID and shift using SQL GROUP BY. Computed columns calculate availability, performance, and quality rate from the raw metrics, producing a shift-level OEE summary table that feeds the engineering dashboard.
Product Category Revenue Rollup
Point-of-sale line items from multiple store files are aggregated with SQL to compute total revenue and units by product category and region. The output feeds the weekly sales analytics board without any manual aggregation step.
See Transform using SQL in action
Input data (left) is transformed using the configuration below. The output table (right) is ready for dashboards or downstream steps.
SELECT Region, Category, SUM(Revenue) AS TotalRevenue FROM InputTable GROUP BY Region, Category ORDER BY Region, CategoryInput Data
| Region | Category | Revenue |
|---|---|---|
| North | Electronics | 15000 |
| North | Clothing | 8000 |
| South | Electronics | 12000 |
| South | Clothing | 9500 |
| North | Electronics | 7000 |
Output Data
| Region | Category | TotalRevenue |
|---|---|---|
| North | Clothing | 8000 |
| North | Electronics | 22000 |
| South | Clothing | 9500 |
| South | Electronics | 12000 |
Configuration
Key fields to configure in the Infoveave workflow builder. Full reference available in the documentation.
Query
A valid SQL SELECT statement operating on the input data. Reference the upstream table using the alias InputTable. Supports standard SQL clauses: SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, and JOIN (when multiple tables are available). Column names must match the exact column names from the upstream activity output.
Frequently asked questions
Everything you need to know about Transform using SQL in Infoveave.
Also in Language Transforms — and what runs before & after
Transformations in the same family as Transform using SQL, often chained together in the same Infoveave workflow.
Part of Infoveave Data Automation
80+ transformations. Zero manual steps.
Transform using SQL is one of over 80 transformation activities available inside Infoveave workflows. Chain transformations together — no code, no exports, no waiting for IT.
Ready to see Infoveave in action?
