https://businessgrowth.ai/ and take that career path quiz I mention earlier in this section. The quiz can give you a head-start in determining where you best fit within the spectrum of data science superhero archetypes.
Chapter 2
Tapping into Critical Aspects of Data Engineering
IN THIS CHAPTER
Though data and artificial intelligence (AI) are extremely interesting topics in the eyes of the public, most laypeople aren’t aware of what data really is or how it’s used to improve people’s lives. This chapter tells the full story about big data, explains where big data comes from and how it’s used, and then outlines the roles that machine learning engineers, data engineers, and data scientists play in the modern data ecosystem. In this chapter, I introduce the fundamental concepts related to storing and processing data for data science so that this information can serve as the basis for laying out your plans for leveraging data science to improve business performance.
Defining Big Data and the Three Vs
I am reluctant to even mention big data in this, the third, edition of Data Science For Dummies. Back about a decade ago, the industry hype was huge over what people called big data — a term that characterizes data that exceeds the processing capacity of conventional database systems because it’s too big, it moves too fast, or it lacks the structural requirements of traditional database architectures.
My reluctance stems from a tragedy I watched unfold across the second decade of the 21st century. Back then, the term big data was so overhyped across industry that countless business leaders made misguided impulse purchases. The narrative in those days went something like this: “If you’re not using big data to develop a competitive advantage for your business, the future of your company is in great peril. And, in order to use big data, you need to have big data storage and processing capabilities that are available only if you invest in a Hadoop cluster.”
Despite its significant drawbacks, Hadoop is, and was, powerful at satisfying one requirement: batch-processing and storing large volumes of data. That's great if your situation requires precisely this type of capability, but the fact is that technology is never a one-size-fits-all sort of thing. If I learned anything from the years I spent building technical and strategic engineering plans for government institutions, it’s this: Before investing in any sort of technology solution, you must always assess the current state of your organization, select an optimal use case, and thoroughly evaluate competing alternatives, all before even considering whether a purchase should be made. This process is so vital to the success of data science initiatives that I cover it extensively in Part 4.
Unfortunately, in almost all cases back then, business leaders bought into Hadoop before having evaluated whether it was an appropriate choice. Vendors sold Hadoop and made lots of money. Most of those projects failed. Most Hadoop vendors went out of business. Corporations got burned on investing in data projects, and the data industry got a bad rap. For any data professional who was working in the field between 2012 and 2015, the term big data represents a blight on the industry.
Despite the setbacks the data industry has faced due to overhype, this fact remains: If companies want to stay competitive, they must be proficient and adept at infusing data insights into their processes, products, as well as their growth and management strategies. This is especially true in light of the digital adoption explosion that occurred as a direct result of the COVID-19 pandemic. Whether your data volumes rank on the terabyte or petabyte scales, data-engineered solutions must be designed to meet requirements for the data’s intended destination and use.
Three characteristics — also called “the three Vs” — define big data: volume, velocity, and variety. Because the three Vs of big data are continually expanding, newer, more innovative data technologies must continuously be developed to manage big data problems.
Grappling with data volume
The lower limit of big data volume starts as low as 1 terabyte, and it has no upper limit. If your organization owns at least 1 terabyte of data, that data technically qualifies as big data.
Handling data velocity
A lot of big data is created by using automated processes and instrumentation nowadays, and because data storage costs are relatively inexpensive, system velocity is, many times, the limiting factor. Keep in mind that big data is low-value. Consequently, you need systems that are able to ingest a lot of it, on short order, to generate timely and valuable insights.
In engineering terms, data velocity is data volume per unit time. Big data enters an average system at velocities ranging between 30 kilobytes (K) per second to as much as 30 gigabytes (GB) per second. Latency is a characteristic of all data systems, and it quantifies the system’s delay in moving data after it has been instructed to do so. Many data-engineered systems are required to have latency less than 100 milliseconds, measured from the time the data is created to the time the system responds.
Throughput is a characteristic that describes a systems capacity for work per unit time. Throughput requirements can easily be as high as 1,000 messages per second in big