7 AI and Advanced Data Transformations
Building on Chapter 6, where we harnessed AI to address data quality challenges, we now turn our focus to advanced data transformations. These transformations are essential in real-world data engineering, enabling us to manage complex data scenarios with precision.
Solving these complex data transformations traditionally requires a wide range of expertise, constant context switching, and familiarity with numerous tools, libraries, and requirements. This can be daunting and inefficient. AI offers a "one size fits all" solution through a conversational interface, simplifying the process by consolidating these diverse needs into a single, adaptable tool..
7.1 Complex Text Processing with Regular Expressions
Regular expressions (regex) are essential tools in data engineering for extracting structured data from unstructured text. One of the most common sources of unstructured text you’ll encounter is the log file. Every application, server, and service you work with produces logs: timestamped entries that record what the system was doing, whether a request succeeded, or why something failed. In theory, these logs are meant to help with troubleshooting and monitoring. In practice, they often look like a wall of chaotic text, timestamps in different formats, error codes mixed with human-readable descriptions, and sometimes even missing or extra fields depending on how the system was configured.