Big Data techniques offer several advantages over traditional techniques.
Hadoop is one Big Data analytics platform.  Hadoop has several ecosystems to perform Big Data Analytics.
Spark is one of such ecosystems. Spark can also run on Hadoop for Big Data Analitycs.
Sensors, tweets, emails, web clickstreams, CRM information, supply chain tools data is flooding into every business, and the businesses that have the most facile processes for divining actionable information from the deluge are going to be the businesses that make the most money.  
This data deluge is not just a problem for large enterprises.  Small businesses also interact with their customers using many channels and have websites, databases and often large amounts of other data to analyze.  
Hence all the buzz around Big data.  
But what does that phrase actually mean, and how does it apply to your business.
The term Big data is ambiguous when does it actually cross the line from just being data.  
The plain truth is that nobody is really sure.  
Some people have definitions like when you have to use tools like machine learning or datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.    
It can be more useful to define Big Data as the data necessary to make decisions that have a positive impact on a business.  
Think about the bigness not in terms of literally how much data there is, but in its potential to help make more money.
If it can be counted, it can be analyzed.  If it can be analyzed, it can be interpreted.  
But what type of count or interpretation can be made from a voice recording of a customer service transaction.  
How are tweets or prose to be interpreted.  What type of information can be gleaned from customer product reviews.  
What happens when those reviews are videos.
Unstructured data is a large part of big data.   
You can get a lot of information from purely structured data, things like the Click Through Rate CTR and conversions from an advertising campaign.   
But thats not going to give you a view into what is actually being said.  What is the conversation.  
In order to delve into the dialog, and whether it is a positive one for your business, you have to get into the unstructured side of things.
The difference between unstructured data and structured data is simple.  
Computers are very, very good at manipulating structured data, and a whole suite of tools has grown up around visualizing and making predictions from this data.  
Structured data is basically numbers at its core how many times a page was visited, how long someone was on your site, where they came in from, what products they bought.   
Unstructured data are things like text say, from a survey or from tweets, or video, or a voice recording of a customer service transaction.
One approach to analyzing the conversation between a company and its customers and partners is to apply brute force manual labor.  
You can read all written communication emails, tweets, and reviews watch the videos listen to the audio recordings.  
Then you can manually convert emotional impressions and interpretations of the conversation into structured data that can be fed into business intelligence tools as a complement to the organizations traditional data sources.
Many businesses take this approach, some more formally than others.  
Some just take a look at their Facebook page likes and comments, or their tweet stream, trying to get a vibe for whether or not things are going well.  
Others ignore unstructured data they dont understand the value of the data or the insights that can be gleaned or they believe that it is too hard for their business to use.