How can we describe big data in an assimilable way? These are large volumes of complex and varied information with different structures. What’s important is that such diversity of disordered information is permanently growing thanks to our online activity. We leave a small brick of data after ourselves whenever we use the Internet.
Hence, big data demand specific tools to manage the enormous motion of information and help process it in an accessible and useful way. You can call this tools big data, which makes it a bit complicated.
There are two versions of description here:
Here you will find the main differences between big data and traditional analytics.
|Big data||Traditional analytics|
|The moment data comes in is being analyzed in real-time.||The stages of data processing are: First, collecting, systematizing, and then analyzing|
|The entire array of accessible types of informations is being analyzed.||Before processing, data are being edited and sorted.|
|The processing of the original data stream is happening in its original form.||While small volumes of data come in, they’re being analyzed in stages.|
|Searching for dependencies and cause-effect relationships are the main parts of the analysis. As a result, the hypothesis is being proposed through the flow of information.||Here testing is based on the before-made hypothesis that uses the available data sets.|
|Thanks to applying the machine learning analysis occur automatically.||The checking of results of data usage is made strictly by human beings.|
There are two ways of storing such data. First are the local data storages that some companies have on-premise. Another option is the cloud solutions that are not expensive and save the day for many companies that can not afford to store it locally.
The methods of getting the correct information from the significant stream vary. Let’s list them to give the broader spectrum:
The industry organizations, keep synchronously analyzing data and behavioral data to help them invent a complex customer profile that they can use in the future. Mainly to: create targeted content for different audiences, measure the general performance of the created content, develop personalized advertisements and suggestions.
Logistics companies have been using analytics to track and report on orders for quite some time. Big data makes it possible to track the status of goods in transit and estimate losses. Real-time data on traffic, weather conditions, and routes for transporting goods are collected. This helps logistics companies reduce risk and improve delivery speed and reliability.
Advertisers are some of the most prominent players in big data. Facebook, Google, Yandex, or any other online giant all track user behavior. As a result, they provide advertisers with a wealth of data to fine-tune campaigns. Take Facebook, for example. Here you can select audiences based on buying intent, website visits, interests, job title, demographics, etc. All this data is collected using big data analysis techniques by Facebook's algorithms.
As examples: accounting of tax revenues, collection, and analysis of data collected on the Internet (news, social networks, forums, etc.) to counter extremism and organized crime, optimization of the transport network, identifying areas of excessive concentration of working, living or unemployed population, the study of prerequisites for the development of territories and so on.
Big data in healthcare is used to improve quality of life, treat diseases, reduce unproductive costs, and predict epidemics. Using big data, hospitals can improve patient care.
Interaction with suppliers and customers, stock analysis, and sales forecasting are just some of the functions that Big Data helps to cope with.
Gathering and analyzing information helps banks fight fraud, work effectively with clients (segmenting, assessing the creditworthiness of clients, offering new products), and manage branches (for example, to predict the queues, the workload of specialists, and so on).
Many machines monitor seismic activity in real-time every day. This allows scientists to predict earthquakes. Even ordinary Internet users also have access to these observation tools: there are various interactive maps.
Traditionally, four big data technologies are distinguished:
1. NoSQL is a database that stores and extracts information in a way that does not follow the traditional logical approach. Unlike relational databases, it does not build tables of normalized sets of standard relationships. The technology began to be used back in the 1960s, but it became popular with the launch of Web 2.0 companies: Facebook, Google, and Amazon. Most NoSQL technologies match data in milliseconds on a "Random" basis and use low-level queries. Such NoSQL solutions are often used:
2. MapReduce. Google invented the technology, but now it is a general term used to define a programming model. This software framework uses distributed parallel processing of large data arrays on ordinary, inexpensive computers. MapReduce software includes functions:
3. Apache Hadoop. A free software platform and framework on the MapReduce programming model, in which distributed storage and processing big data sets are organized. Tasks are divided into small, isolated fragments, each of which can be run on a separate node of a cluster of serial computers. This compartmentalization allows information to be automatically processed when hardware fails. Among the software associated with Hadoop are:
4. R programming language. Used in statistical calculations to analyze and display data in graphical form. The language is used in statistical analysis, including linear and nonlinear regression, classical statistical tests, in the analysis of time series (series), cluster analysis, and so on.
Techniques for processing big data are constantly being updated and are now being applied:
On the other hand, everyone's information footprint helps humanity interact: selling and buying goods, transferring and receiving money, helping people with everyday lives, giving immediate solutions for businesses, or even predicting cataclysms and calculating resources to deal with their consequences. Big data is the future of our digital lives.
It is necessary to remember that big data technologies depend on the volume, speed, and variety of information flows. Big data analytics tasks are to isolate and predict patterns based on unstructured data of various kinds, vast volumes of which come from different sources in fractions of a millisecond. You may want to know more about the big data solutions we deliver here.