Data transformation is like taking a messy pile of information and turning it into something neat and organized that we can easily understand.
Imagine you have a jumble of numbers, names, and dates on a piece of paper. Data transformation is the process of tidying up that information, so it's easier to work with. You might start by fixing any errors or mistakes, like correcting misspelled names or removing duplicate entries.
By going through these steps of data transformation, we can take messy and disorganized data and turn it into a clean, organized, and understandable format. This makes it easier for us to discover insights, identify patterns, and make informed decisions based on the information we have.
I am excited to participate in the upcoming 30-day Power Query Challenge, where I will showcase my expertise in data analytics and transformation. Throughout the challenge, I will be diligently working on optimizing data manipulation processes using advanced Query Folding techniques.
During the challenge, my primary focus will be on streamlining data transformation steps to enhance overall performance and efficiency.
In each solution I share within the dedicated repository, I will provide comprehensive insights into the M Query code. This code, extracted from the advanced editor, will illustrate the intricate steps involved in the data transformation process. It will encompass techniques such as data cleansing, filtering, aggregating, and merging, all tailored to meet specific analytical requirements.
Find more details on the challenge here: Link