Today we are in the age of digitization. People are generating data in different ways: they take pictures, upload images, write blogs, comment on someone's blog or picture, change their status on social networking sites, tweet on Twitter, update details on LinkedIn, do financial transactions, write emails, store data on the cloud, and so on. Data size has grown not only in the personal space but also in professional services, where people have to deal with a humongous amount of data. Think of the data managed by players such as Google, Facebook, the New York Stock Exchange, Amazon, and many others. For this data tsunami, we need the appropriate tools to fetch data, in an organized way, that can be used in various fields, such as scientific research, real-time traffic, fighting crime, fraud detection, digital personalization, and so on. All of this data needs to be captured, stored, searched, shared, transferred, analyzed, and visualized.

Analyzing structured, unstructured, or semi-structured ubiquitous data helps us discover hidden patterns, market trends, correlations, and personal preferences. With the help of the right tools to process and analyze data, organizations can expect much better marketing plans, additional revenue opportunities, improved customer services, healthier operational efficiency, competitive benefits, and much more. It is important to not only store data but also process it in order to generate information that is necessary. Every company collects data and uses it; however, to potentially flourish more effectively, a company needs to search relevant data. Every company must carve out direct search-produced data, which can improve their business either directly or indirectly. 

Okay, now you have Solr, which is generally referred to as search server, and you are doing searches. Is that what you need? Hold on! This allows a lot more than a simple search. So get ready and hold your breath to take a deep dive into Solr—a scalable, flexible, and enterprise NoSQL search platform!

We will go through the following topics in this chapter: