Search

Connectors

Contact support

Data transformations

Data can be transformed for many different reasons. Here are a few examples:

  • Changing a date format
  • Converting from one currency to another
  • Unifying the data before doing aggregations (e.g. Sum)
  • Ability to join data from different tables
  • Etc.

Data can be transformed in Peliqan in different ways:

  1. Using SQL queries in the Peliqan Query Editor
  2. Using Low-code Python code in the Peliqan Data App editor

Transforming data in Peliqan

Using SQL queries

Add a new table of type “SQL query”, and write an SQL query that transforms the data.

Using low-code Python

See Building Data Apps for more information. You can for example process the rows of a table in a Python script, and write the result to the same table (in other columns), or write to a separate output table.

Various Data Transformation scenarios

Here are examples of common data transformations for various use cases:

Convert numeric status fields to readable textMerge data from multiple similar sources (e.g. SaaS)Currency conversionsMapping charts of accounts from different countries (accounting)

Overview of available transformations in Peliqan

ROW TRANSFORMATIONS
Filters
Apply filters to include/exclude rows based on various criteria in the Grid view interface
Advanced filters
Write SQL queries to filter your data
Remove duplicates
Using a Python script
Edit rows
Edit data in a spreadsheet-like Grid view interface
COLUMN TRANSFORMATIONS
Arithmetic
Apply calculations & aggregations in SQL
Case / if
Apply “if” logic in SQL
Comparison
Compare numbers, dates, text in SQL
Text manipulation
Apply text manipulations in SQL
Date manipulation
Apply date manipulations in SQL
SQL queries
Write SQL queries to apply any type of column transformation.
Obfuscate data
Obfuscate data, e.g. for GDPR reasons, using a Python script
TABLE TRANSFORMATIONS
Join
Join tables
Aggregations
Aggregate data using sum, avg, mean etc.
Union
Union two or more tables into a new table.
SQL
Write SQL queries to apply any type of table transformation, using JOIN, UNION, GROUP BY statements etc.
Low-code Python
Use low-code Python scripts to process data and to create new tables and to perform writeback (Reverse ETL) to SaaS applications.