Big data is so popular and so talked about that people forget that it’s still a new field. Digital data has been used and has been gathered for a little more than a decade; it’s useful for more than just big companies like Google or Amazon. Every company can benefit from big data in some way as it aggregates the data. Data exhaust and metadata are also created here and all three are integral for business analytics. With all the information that’s out there, it still isn’t well known with more myths than facts being shared. Here are five myths about Big Data, along with the debunking of them.
5 Myths of Big Data
Myth #1. Big Data is Only About Huge Amounts of Data – The three elements of Big Data are Variety, Velocity, and Volume. How much data is gathered is the least important of the three. Volume is the starting point, but it’s fluid and changes all the time. The other two are better indicators to go from. Variety deals with the different data types, be it files, video, social media posts, etc. Velocity is the rate of change and how quickly it has to be used before it changes again.
Myth #2. You Have to Use Hadoop for Big Data – Hadoop is open-source software from Apache to use with Big Data. Consequently, Big Data is too big and too varied to be found, sorted and defined. You really can’t just use one program, it takes many programs. Hadoop is one of three different classes to work with Big Data. The other two are NoSQL and Massively Parallel Processing (MPP). On top of that, not all Hadoop components are built to work with Big Data and can be replaced with something that’ll work better.
Myth #3. It Means Unstructured Data – A better term for it is multi-structured as there are so many forms and types of data that can be gathered. Data models are built when the data is going to be used.
Myth #4. It’s for Social Media Feeds and Sentiment Analysis Only – It does do this, but it isn’t the only thing that Big Data analyzes for you. Any type of data is possible for analysis through using Big Data. Don’t restrict yourself from its full potential.
Myth #5. NoSQL means not only SQL – These types of data stores offer different ways to find and sort the data. Technologies here includes key-value stores, document-oriented databases, graph databases, and big table structures to name a few. SQL access can use many tools to complete its work.