Let’s assume that you’re working on a web application. In the simplest case, it probably has the stereotypical three-tier architecture: you have some clients (which may be web browsers, or mobile apps, or both), which make requests to a web application running on your servers. The web application is where your application code or “business logic” lives.
Whenever the application wants to remember something for the future, it stores it in a database. And whenever the application wants to look up something that it stored previously, it queries the database. This approach is simple to understand and works pretty well.
However, things usually don’t stay so simple for long. Perhaps you get more users, making more requests, your database gets slow, and you add a cache to speed it up – perhaps memcached or Redis, for example. Perhaps you need to add full-text search to your application, and the basic search facility built into your database is not good enough, so you end up setting a separate indexing service such as Elasticsearch or Solr.
Perhaps you need to do some graph operations that are not efficient on a relational or document database, for example for social features or recommendations, so you add a separate graph index to your system. Perhaps you need to move some expensive operations out of the web request flow, and into an asynchronous background process, so you add a message queue which lets you send jobs to your background workers.
By now, other parts of the system are getting slow again, so you add another cache. More caches always make things faster, right? But now you have a lot of systems and services, so you need to add metrics and monitoring so that you can see whether they are actually working, and the metrics system is another system in its own right.
Next, you want to send notifications, such as email or push notifications to your users, so you chain a notification system off the side of the job queue for background workers, and it perhaps needs some kind of database of its own to keep track of stuff. But now you’re generating a lot of data that needs to be analysed, and you can’t have your business analysts running big expensive queries on your main database, so you add Hadoop or a data warehouse, and load the data from the database into it.
Now that your business analytics are working, you find that your search system is no longer keeping up… but you realise that since you have all the data in HDFS anyway, you could actually build your search indexes in Hadoop and push them out to the search servers, and the system just keeps getting more and more complicated…

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