Transformation Patterns, Cleansing, and Normalization

In this chapter, we’ll learn about transformation patterns and their role in data management. The Lambda, Kappa, and Microservice architectural patterns will be covered in the following sections. We’ll also cover important data transformation methods, such as cleansing, normalization, masking, de-duplication, enrichment, validation, and standardization.

Data workers, like you, must understand these transformation patterns and methods. In a data-driven world, the ability to analyze raw data is invaluable. This expertise is crucial for data scientists preparing data for machine learning models, analysts gaining insights, and database administrators assuring data governance and security.

The Lambda, Kappa, and Microservice designs enable you to construct robust data pipelines for large and diversified data sources. Understanding data infrastructure construction is crucial in a business setting where fast and accurate...

Transformation patterns

Data transformation has given rise to several different architectural patterns for more effective data management, processing, and storage. These patterns offer a roadmap for developing software that can cope with the growing volume, velocity, and variety of data. The Lambda, Kappa, and Microservice architectures will be discussed in this section as they are all examples of common transformation patterns.

Choosing the correct transformation pattern is crucial if a business is to maximize the value of its data and data assets. Different situations call for the use of different design patterns due to their strengths and weaknesses.

When deciding between different transformation patterns, it’s important to think about things such as scalability, flexibility, maintainability, and fault tolerance. Consider your organization’s specific requirements and constraints, such as its available funds, personnel, and technology.

In the following sections...

Data cleansing and normalization

Having high-quality data is essential in the data processing and transformation industry. Messy, inconsistent, or incorrect data can produce questionable conclusions and should be avoided at all costs. This is where the need for data normalization and cleansing becomes apparent. In this section, we’ll delve into these two methods and examine their significance in preserving data quality.

Data cleansing, also known as data scrubbing, is the process of inspecting data for mistakes and then fixing (or removing) them. Errors in data entry, technical difficulties, and even representational differences can all contribute to these problems. Your data will be more useful for analysis, reporting, and decision-making if you take the time to clean it first.

Here are a few examples of typical data-cleansing activities:

• Fixing misspelled words and typos
• Creating a universal time and date format
• Adding or editing data to complete a record...

Summary

In this chapter, we explored various architectural patterns and techniques for data transformation and cleansing. We understood the Lambda, Kappa, and Microservice architectures, highlighting their strengths and use cases. We also delved into data cleansing and normalization, discussing techniques such as data masking, de-duplication, enrichment, validation, and standardization. Finally, we provided a comprehensive summary of this chapter’s key concepts and concluded with an overview of the topics covered.

Moving on to the next chapter, the focus shifts to exposing and accessing data through different technologies and APIs.