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article | 02 Sep 2022
Entering the world of big data
Przemysław Pala
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We live in an information age where almost all of our actions leave a digital footprint. It’s like the informational tail dragging along us. It led to a situation when volumes of information and data are so large and complex that traditional ways are no longer suitable for processing them. We call them big data.
 
 

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:

  • great amounts of digital data
  • a set of analytical tools and methods for their processing

 

Differences from traditional analytics

Big data is not just big. It is massive, and its volume is growing exponentially. Therefore, the tools of traditional analytics, which use human labor and desktop computers, cannot deal with the processing and analysis of so-called big data.

 

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.

 

 

The processing of big data

What you mostly need to process the data are modern computer systems. Such tools provide the adjusted power, speed, and flexibility that enable access to huge volumes and different types of big data.

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:

  • Machine learning
  • Data mining
  • Data visualization
  • A/B-testing.
  • Simulation modeling

 

Big Data usage by examples

 

Media & Entertainment

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

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.

 

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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.

 

Governmental structures

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.

 

Medicare

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.

 

Retail and wholesale

Interaction with suppliers and customers, stock analysis, and sales forecasting are just some of the functions that Big Data helps to cope with.

 

Banking industry

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).

 

Prevention of natural and man-made disasters

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.

 

 

Solutions for big data 

Big data technologies now include solutions that can process very large amounts of information.

 

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:

  • MongoDB is a cross-platform document-oriented database management system with JSON and dynamic schema support;
  • Apache Cassandra - a scalable database that focuses on fault tolerance;
  • HBase - a scalable distributed database with support for high-volume structured storage, and so on.

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:

  • Map handles key/value pairs and generates a set of intermediate key/value pairs;
  • Reduce, which brings together all the intermediate values associated with the same intermediate key.

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:

  • Apache Ambari - a tool for managing and monitoring Hadoop clusters;
  • Apache Avro - a data serialization system;
  • Apache Hive - a data warehouse infrastructure that provides data aggregation;
  • Apache Pig, a high-level data flow language and software framework for parallel computing;
  • Apache Spark, a high-performance engine for processing data stored in a Hadoop cluster, and so on.

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.

 

Analysis and processing techniques of big data

Big data is a super-large amount of information that does not make sense until it is analytically processed. Big data can only be used after qualitative analysis.

 

Techniques for processing big data are constantly being updated and are now being applied:

  • classification - needed to predict customer behavior in a particular segment
  • cluster analysis - identifies common characteristics, on the basis of which the data is classified into groups
  • crowdsourcing - collects a variety of information from a large number of sources
  • data mining - identifies unknown, but valuable information that will help make the right decision
  • machine learning - creates self-learning neural networks that process information quickly and with increasing quality
  • teacherless learning - a way of machine learning that allows a system to spontaneously solve assigned tasks without human input; it is used to uncover hidden functional relationships in data
  • signal processing - examines digital signals that change in order to recognize them against the background of information noise and analyze them
  • blending and integration - it translates unstructured data into a single format
  • visualization - the results of the analysis are presented in the form of diagrams and animations

 

The future of big data

 

Whether you like the idea of big data or not, it will expand rapidly, since it’s the mark everyone leaves online. Even if you know what big data is, it is almost impossible to keep your data safe. Everything you do is stored on the Internet and becomes part of the big data that is used everywhere. It does sound scary to us, too, at one point. Let’s try to look at it from a different angle now.

 

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.