Introduction
As the internet has grown and the amount of information available has increased, so too have the tools for analyzing it. Large-scale data—or “big data”—is a phenomenon that has been around since the dawn of computing but is only now starting to reach maturity.
Large Data Sets
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
It’s not just about size. It also refers to the structure and content of the data set. Big Data can be gathered from many sources: sensors, social networks and other online interactions, scientific instruments such as telescopes, satellites and many more.
Big Data Defined
The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
Big data is a term used to describe large and complex data sets that are difficult to process using traditional data processing applications. Big data is commonly associated with machine learning, predictive analytics and business intelligence.
Data Set Structure
A big data set is no longer characterized by its size in bytes, but by its structure – it may still be stored in a simple flat file – and its content. Data sets can contain structured data such as user information or product prices, unstructured information like videos or audio files, as well as real-time sensor data from machines.
The only thing for sure about big data is that it will be big! No two big data sets are exactly alike; every new project brings new challenges and opportunities.
Four Key Dimensions of Big Data
There are four key dimensions included in big data: volume (the amount of data), velocity (the speed of data in and out), variety (the range of data types and sources), and veracity (the quality and trustworthiness of the data). The first three—volume, velocity, and variety—are easy to understand. Veracity is a bit more complex.
One way to think about it is that the more varied your sources are, the harder it will be to trust them all equally. So if you can’t figure out how much weight to put on any one source, you may need to rely on multiple sources to get an accurate picture of what’s going on.
Data Set Growth
Data sets grow rapidly. For example, Walmart handles more than 1 million customer transactions every hour, which are captured and stored in databased for analysis. In this case, the data is accessed by thousands of employees who use it to make decisions about products and services sold in their stores.
As an organization grows and becomes more efficient at collecting data from its customers and other sources (such as sensors), it may find itself with a wealth of information that can be used to make better decisions or improve business processes or offerings.
Data is created by many sources: people using mobile devices; sensors monitoring weather conditions or the performance of equipment; machines communicating via the Internet of Things (IoT); credit card purchases made online; etc. Data can also be stored in various formats—for example, images, videos and text documents containing different types of information relevant to different industry verticals (e-commerce websites collect customer preferences through surveys while manufacturers monitor production lines via video cameras).
The Complexity of Data Sets
The complexity arises due to the variety of forms that these large volumes of unstructured and semi-structured data can take which does not fit well into traditional row/column databases. Unstructured data may be text, video, or audio. Semi-structured data is data that has a schema (or structure), but it is not fixed. Structured data is data that has a fixed schema and conforms to the relational model used by most common databases today.
Storing Big Data
In order to keep up with the flood of information from different sources, businesses need an easy way to store this kind of information. This can be done using Big Data analytics tools, which allow for the collection and analysis of large amounts of data.
Conclusion
When you have big data, it’s important to know how it works and what tools can handle the workload. There are many different ways to process big data, but they all can be classified into three categories: batch processing, stream processing or real-time event processing (RTEP).