Data Engineering for Beginners

CHAPTER 11Big Data and Distributed Systems

In my experience, organizations across industries work with data of all sizes, ranging from gigabytes to petabytes and, in some cases, even more. Earlier chapters covered the basics of databases and data processing. Still, when data gets so large, it requires a completely different method of processing, one that is faster, more efficient, and built to scale.

Traditional databases and single-server architectures—that is, database systems that run entirely on a single machine—struggle to keep up with the scale and complexity of modern data because when large datasets are processed on a single machine, it leads to slow performance and a lot of scalability issues. Looking at big companies like Netflix, Google, and Amazon, which manage massive amounts of data without their systems crashing, raises an important question: What makes this possible? What technologies and strategies allow them to handle such enormous workloads seamlessly?

The answer lies in distributed computing and understanding the basic features of big data. With the right systems, businesses can process huge amounts of data more efficiently.

IN THIS CHAPTER, WE WILL EXPLORE:

• The fundamentals of big data
• The five V’s of big data
• Key principles of distributed systems and their components
• An overview of big data processing and frameworks
• The design architectures of Apache Spark and Hadoop
• Various big data file types
• Choosing the right file types for big data projects

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