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

- Correcting data errors
- Unifying the data before doing aggregations (e.g. Sum)
- Ability to join data from different tables

Data can be transformed in Peliqan in three different ways:

- Using
**Formulas**in the Peliqan Spreadsheet Editor - Using
**SQL queries**in the Peliqan Query Editor - Using
**Low-code Python code**in the Peliqan Data App editor

# Transforming data in Peliqan

## 1. Using formulas

Add a new column to a table using column type “Formula”. Write a formula to transform data from other columns.

## 2. Using SQL queries

Add a new table of type “SQL query”, and write your own SQL query to create a new table. You can use any SQL statement to transform the data.

You can also define your own Database functions (Custom functions) which can be used in your SQL queries.

## 3. 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 table, or write to a report file (PDF, Google Sheet etc.).

# Various Data Transformation scenarios

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

# Overview of available transformations in Peliqan

ROW TRANSFORMATIONS | |

Filters | Apply filters to include/exclude rows based on various criteria |

Advanced filters | Write SQL queries to filter your data |

Remove duplicates | Coming soon |

Edit rows | Edit data in a spreadsheet-like interface |

COLUMN TRANSFORMATIONS | |

Arithmetic | Apply calculations using easy formulas |

Case / if | Apply “if” logic using easy formulas |

Comparison | Compare numbers, dates, text using easy formulas |

Text manipulation | Apply text manipulation using easy formulas |

Date manipulation | Apply date manipulation using easy formulas |

SQL queries | Write SQL queries to apply any type of column transformation. |

Custom SQL functions | Define your own custom functions in JS and use them in your SQL queries. |

Obfuscate data | Obfuscate data, e.g. for GDPR reasons.
Coming soon |

TABLE TRANSFORMATIONS | |

Join | Join tables |

Fuzzy join | Apply to combine tables without using a unique key.
E.g. combine customer data from different sources.fuzzy join |

Aggregations | Aggregate data using sum, avg, mean etc. |

Union | Union two or more tables into a new table. |

Advanced | 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. |