Computers have come a long way — they can store huge volumes of data and process it at incredible speeds. But there's one problem: data is growing even faster. So fast, in fact, that no matter how powerful a single machine is, it eventually hits a wall — either it can't process the data fast enough, or it simply runs out of space. The only scalable solution is to distribute the data and computation across multiple machines that work together as one.

The Need for Distribution

Think about what happens when you ask your computer to do too much. It slows down, runs out of memory, or in the worst case — it crashes. If these problems bother you, the usual fix is to upgrade your hardware: buy a better CPU, add more RAM, or replace your hard drive. This approach is known as vertical scaling.

Vertical scaling works, but only to a certain point, as all hardware has limits. Even the most powerful machine can crash or struggle with tasks at hand. That's why large-scale systems turn to horizontal scaling.

Instead of relying on one machine to do everything, horizontal scaling distributes tasks across multiple machines — and that's the idea behind distributed systems.

What Is a Distributed System?

Each computer in a distributed system is called a node. These nodes communicate over a network, coordinate tasks, and share data. Together, they can handle workloads far larger — and more reliably — than any single machine could manage.

Distributed systems are everywhere: from global search engines and real-time analytics platforms to social networks, cloud storage, and online retail. They're not just a clever optimization — they're the backbone of modern computing.

Why Are Distributed Systems Challenging?

Distributed systems are very similar to a code that runs in multiple threads. They have problems with:

• Latency: communicating over a network is much slower than accessing local memory;
• Concurrency: multiple machines working at once increases the risk of race conditions and state conflicts;
• Load balancing: without proper distribution of tasks, some machines will be overloaded, while others will idle;
• Data consistency: when data is replicated across nodes, keeping it in sync is difficult;
• Partial failures: machines or networks can fail independently;
• Debugging and observability: tracking down problems across multiple nodes is harder than debugging a single process;
• Security: every node is a potential vulnerability.

Despite these challenges, distributed systems make large-scale data processing and real-time analytics possible — and they’re a key foundation for tools like Apache Spark, which we'll explore later.