extract transformation load data agggregation

Making data transformations usable and impactful requires documentation.ĭocumentation covers and outlines the purpose of the data model and transformation in the first place, as well as defines key metrics and business logic that has been applied.ĭata is like a living and breathing organism–more of it comes into your business every day and these processes have to be applied on a recurring basis to make use of data.ĭata automation software standardizes and automates the process so that data can continuously be used to drive value. When the testing has proven the data to be in good standing, you can expose results to end users. Once data is modeled and ready to go, you can test it out in action by ensuring column values fall into the expected range, checking model relations line up, etc. Heavy transformation involves adding business logic, data aggregation, and the like. Light transformation includes renaming tables and fields, casting fields correctly, and creating uniformity. Transformation can be either light or heavy. This is the process of editing the format from the source system into the right format for the target source.

Executing Code: Once the code is ready, the work really begins.Generating Code: At this step, the code generation is performed so that you can transform data into the required format.This step consists of two main efforts, namely: At this step, you’re aware of how the data is structured as well as how it needs to be structured. Data Transformationĭuring the data transformation stage, the main work takes place. Data mapping sets the action plan for the data and can end up being time-consuming without the help of automation software. During this phase, you get to see how the data looks and identify if any information is missing. Once in the data warehouse, it’s time for data exploration, or data mapping. Or, if you’re using an ELT workflow, then the data will be extracted from its original sources and loaded into the target data warehouse. A data profiling tool is helpful at this stage. You begin by identifying and understanding the source format of your data. To reap insights from data, it must be transformed from its raw starting point into usable information.ĭata transformation steps look like this: 1. What are the Steps in Data Transformation?

Extract transformation load data agggregation