Nexadata Pipelines offers a variety of data transformation tools to help you prepare and clean your data efficiently. Each transformation serves a specific purpose, from reorganizing data to adjusting values and making the dataset easier to analyze.

Here is a summary with detailed links to each transformation, including guidance on using each transformation with natural language, advanced options, and practical use cases.

Duplicate an existing column within your dataset and specify where the duplicate should be placed. This is useful for preserving original data while creating variations.

Refine your dataset by including or excluding rows based on custom criteria. This transformation allows you to focus on specific data subsets or filter out unnecessary information.

Easily adjust the sign of numerical values. Use custom conditions to automatically change positive values to negative or vice versa, ideal for scenarios requiring quick adjustments.

Aggregate data by grouping it based on a specified column and applying functions such as sum, count, or average. This transformation is particularly useful for data summarization and analysis.

Add new columns to your dataset with either predefined values or formulas. This enables the creation of derived data or new calculated fields directly within your dataset.

Sequentially number each row in your dataset. This transformation provides unique row identifiers, which can be useful for tracking and referencing.

Combine datasets based on a common key. Choose from graphical joins like inner, outer, left, and right to create a comprehensive, unified dataset tailored to your needs.

Selectively retain only the necessary columns, removing all others. This transformation streamlines your dataset by preserving only the essential data.

Convert text in selected columns to lowercase. This transformation helps standardize your data, particularly useful when case consistency is needed.

Combine the contents of selected columns into a single column, with a customizable delimiter. This can simplify your data structure by consolidating related information.

Delete unwanted columns from your dataset. Removing non-essential columns can improve data clarity and processing speed.

Update column names to be more descriptive or to match naming conventions. Consistent naming improves data readability and reduces confusion.

Find and replace specific values within a column. This transformation supports exact matches or regex patterns, making it flexible for various data correction needs.

Reorder columns within your dataset, positioning them at the beginning, end, or relative to other columns. Use this transformation to prioritize important data.

Arrange the dataset in ascending or descending order based on selected columns. Sorting allows you to organize data for easier viewing and analysis.

Divide the content of a single column into multiple columns based on a specified delimiter. This transformation is useful for breaking down complex data entries.

Calculate the total of values within a selected numeric column and output the result in a single column. This transformation is helpful for quick aggregations.

Convert text in selected columns to uppercase for standardization. This ensures consistency in text formatting across your dataset.