# What Is Data Transformation?

## Метаданные

- **Канал:** 365 Data Science
- **YouTube:** https://www.youtube.com/watch?v=iYQAqgOx5JA
- **Источник:** https://ekstraktznaniy.ru/video/44430

## Транскрипт

### Segment 1 (00:00 - 05:00) []

Hello and welcome to Data Transformation Concepts! Today, we're going to explore how raw data is cleaned, standardized, and transformed into valuable insights. Think of transformation as baking a cake. You mix different ingredients in precise proportions to create something new and delicious. Your raw data is the ingredients; transformation is the recipe that turns it into a treat for decision-makers. Let's dive in and explore transformations in more detail step-by-step. Data cleaning is the first step in any transformation process. Think of it as washing your vegetables before cooking. You need to remove the dirt and any imperfections so that you're left with only the good stuff. In the context of data, this means identifying and correcting errors, removing duplicates, and ensuring consistency across your data. Hand in hand with cleaning goes data standardization. It's about ensuring that data follows a consistent design, like all dates being in the same format or that text is consistently capitalized. This step makes your data reliable and easier to work with down the line. Once the data is clean, we move on to transformation. This is where data is reshaped to fit the needs of the final analysis. There are several techniques for transformation, including: * Normalization, which deals with adjusting values measured on different scales to a common scale. * Aggregation, that summarizes data, such as calculating totals or averages. * And derivation, that is, creating new data fields from existing ones, like computing profit margins from sales and cost data. A very important piece of the transformation process is handling of missing data as it's a common challenge with most data sources. Let's look at three key strategies for handling missing data: * First is imputation. This technique is used to fill in the missing values with estimated or calculated values using the available data, such as the average or median of all the non-missing data. * Next is removal. Sometimes, if the missing data is minimal, it might be easier to simply remove those records. * And the final one is flagging, that is marking missing values so that they can be accounted for in the analysis. Each of these methods provides a different type of value to dataset. The key is to choose a method that minimizes bias while preserving as much useful information as possible. To identify missing values, formatting issues, or any other potential issues with the data, we use data validation and quality rules. Validation rules ensure that the data meets specific criteria before it moves on to the next stage. Imagine quality control in a factory: every product is inspected to ensure it meets the standard. Similarly, in data transformation, validation rules check that data values fall within expected ranges, that mandatory fields are populated, and that formats are correct. Data quality checks work on a tangent and are automated processes that run after the transformation to help catch errors early on. This step is critical for maintaining the integrity and reliability of your data. Once the data is loaded into the warehouse and it has cleared the validation and quality checks, confirming data is clean and of good quality there are some transformation steps that help make the data organized and easy to work with downstream. To immediate next steps are data standardization and normalization. These are key processes that further enhance data consistency. Standardization ensures uniformity across datasets, while normalization reduces redundancy and improves data integrity. It's like organizing a library where books are sorted not only by genre but also by author and publication date, making it easier to locate the exact book you need. Data standardization ensures that data from different sources is consistent in format and structure. Data normalization on the other hand organizes data to reduce redundancy and improve database efficiency by dividing it into related tables. Both these transformation steps ensure consistent, accurate, and efficient data storage, leading to reliable analysis and decision-making in data warehousing. To sum up, data transformation is a critical step that turns messy, raw data into a polished

### Segment 2 (05:00 - 05:00) [5:00]

actionable asset. We've explored everything from data cleaning and standardization, through various transformation techniques, to handling missing data and enriching datasets with standardization and normalization. Remember, just like assembling a complex puzzle or baking a perfect cake, attention to detail in data transformation leads to outstanding results in your analytics projects. Next, we'll talk about the different strategies for loading data into the warehouse and automating the complete warehousing lifecycle.