Storage is the cornerstone of the data engineering lifecycle (Figure 6-1) and underlies its major stages—ingestion, transformation, and serving. Data gets stored many times as it moves through the lifecycle. To paraphrase an old saying, it’s storage all the way down. Whether data is needed seconds, minutes, days, months, or years later, it must persist in storage until systems are ready to consume it for further processing and transmission. Knowing the use case of the data and the way you will retrieve it in the future is the first step to choosing the proper storage solutions for your data architecture.
Figure 6-1. Storage plays a central role in the data engineering lifecycle
We also discussed storage in Chapter 5, but with a difference in focus and domain of control. Source systems are generally not maintained or controlled by data engineers. The storage that data engineers handle directly, which we’ll focus on in this chapter, encompasses the data engineering lifecycle stages of ingesting data from source systems to serving data to deliver value with analytics, data science, etc. Many forms of storage undercut the entire data engineering lifecycle in some fashion.
To understand storage, we’re going to start by studying the raw ingredients that compose storage systems, including hard drives, solid state drives, and system memory (see Figure 6-2). It’s essential ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month, and much more.
