Big data is a term that is popular is any these days but many still don’t know the answer to the question What is Big Data? So we will seek to answer this question in the clearest way possible and explain what it entails. Another term that is getting more and more popular is fast data and in this article, we will also explain fast data and discuss the difference between big data and fast data.
Big data is essentially defined by certain features. Data has to process certain capabilities before it can be classified as big data. Below we discuss some of the main features that characterize big data
Characteristics of Big Data
In order for big data to be classified as such, it must have a highly scalable analytics process. There are platform and software available that help data analysts to scale their big data. Getting the right software is important because with the right scaling software lots and lots of data can be analyzed without fear of degradation. Keep in mind that these tools are a lot more sophisticated than basic tools like SQL or MATLAB.
Big data is flexible. If you have a pile-up of data but it’s not flexible, then it cannot be classified as big data. Big data is stored in different forms and formats and should be able to be accessed and processed using different tools. So in the traditional sense of things, big data is basically unstructured data. This lack of structure is what makes it flexible. Fast data transformation is an essential part of Big Data, as is the ability to work with unstructured data.
Big data should be able to provide real-time changes and real-time results. Now everything is so fast and dynamic, there is not a time to wait days weeks or even hours for data analytics results. Big data should be able to be accessed and interpreted in real time. For example in a healthcare organization, big data should be able to accessed and interpreted in real time as the doctor makes is round visiting patient with his iPad device in hand used for storing new patient information and accessing old patient information simultaneously.
Machine learning is a pretty important application used to access and interpret big data, It is not compulsory but it is becoming increasingly important and necessary that it is present wherever big data is. Machine learning applications need to be used for big data and this sets big data apart from traditional data.
Data quality is important not just for big data but for all data. If big data has little integrity, it is useless and takes up resources that end up wasted. Big data contains complex data sets and it is important that this has great quality. Attention to data quality is a core feature of any effective Big Data workflow.
Fast data can be said to have originated from both big data and smart data. The big data revolution has been the underlying revolution that set the foundation for smart data, fast data, and actionable data. A continued attempt to understand data has lead to many protocols and programs being built that has led to standardized ways of understanding, processing, interpreting and storing data. Fast data comes with its own sets of challenges but they don’t measure up to its benefits.
When it comes to fast data, the conversation shifts from talking about gigabytes and terabytes to talking about speed and velocity of data. Like finding out how many megabytes or terabytes can be processed a second or in an hour. Volume in this sense is now measured in terms of time.
Fast data means that big data is not just big but it is also fast. Fast data means big data is easily accessible and can be better used and interpreted into information. Imagine if you had folders of research data stored on your computer or on the cloud and this data was relevant to a paper you were writing. If you can’t access and sort through this data in a timely manner, your process becomes slow and the value of the data drops. In this lies the importance of fast data.
Big Data vs Fast Data
Below I outline the difference between big data and fast data …
|Big Data||Fast Data|
|In the healthcare industry, historical patient data is analyzed to come up with future predictions||With fast data doctors can provide insightful predictions based on real-time data and implement them in a shorter period of time|
|In the automotive industry, car engineers analyze large sets of crash tests data and data on other sensors such as safety measure||With fast data the cars can provide real-time data such as traffic information and alerts./\|
|In e-commerce. Stores can predict what to stock or which goods are selling the most based on weekly, monthly, or quarterly data analysis.||With fast data, stores can get real-time updates on which products drive the most traffic and which products are gaining the most interest, which products are in a customers cart and which were removed etc|
|In the financial industry, banks and credit card companies can access the viability of a customer based on analysis of their financial history.||Fast data enables features like “real-time fraud alert”|