In this article, I want to tackle some of the biggest challenges data engineers face when working with pipelines throughout the data lifecycle. Understanding how to manage the data lifecycle is key in our constantly changing field. As a data engineer, I often deal with huge volumes of different types of data, including unstructured data, coming from various sources like databases, data lakes, and third-party APIs. These factors can make managing critical data really tough. We’ll cover all the important stages of data processing, from collection and analysis to storage and destruction, and I’ll share the best practices I use every day.
Data lifecycle management
Data lifecycle management enables businesses with a strategic and regulated approach to organising and managing data from source to destination or its final state such as archiving or destruction.
Essentially, this is a set of policies to maximise the value of data throughout its useful life, from data creation to destruction where it becomes obsolete or needs to be destroyed due to compliance regulations.
The typical data lifecycle follows a well-known ETL pattern.