WHY BIG DATA
IS A MUST-HAVE

(32)

Sergey Lobko-Lobanovsky
Chief Executive Officer, Geomotiv
Published: Mar 15, 2019

So, what is Big Data? The thing everyone is talking about for a number of years already. It may be a buzzword, but not many people really know what it is. Let’s start from the beginning and clarify its definition.

What is Big Data at All?

Big Data is a kit of technologies, methods, and approaches that apply to enormously huge data sets. These data sets can be both structured and unstructured. The data may appear in form of:

  • User logs;
  • Clients information from their profiles;
  • A list of tax dodgers;
  • Information about transactions made by all clients of a particular bank and so on.

These are just a few examples of data sources and their number is constantly increasing.

This data is extremely useful for marketing analyses, targeting, customer experience. It helps to improve and reveal various behavior scenarios. Proper use of such information may show, for example, whether an advertising campaign was successful. 

What’s the Difference with Traditional Databases?

Well, the thing is that databases, including Data Warehouses, appear to be more of an architecture, whereas Big Data is a set of technologies and solutions. Apart from simply storing the data, Big Data processes, structures, and analyzes it. Thus, it gives news opportunities to gather and work with huge amounts of incoming data. 

To put it simply, here’s an example. Imagine a giant bookshop with tons of useful titles displayed with no regard to sections. Shakespeare lies next to Hawking, Tolkien adjoins writings of Socrates, and Lovecraft shares the bookcase with children’s literature. Sorting it manually will cause terrible time spending. However, using Big Data solutions you can put the books in their places and help readers find the fiction they like. Moreover, it displays the amount of books left, their prices, which group of visitors prefers this or that section, and which literature is in great demand now. 

The Markers of Big Data

At first, Big Data was described by three main characteristics that may be illustrated by three V’s:

  • Volume means the quantity of processed data. Big Data works with masses of information being able to analyse it with no breaks. It must be more of 100 GB a day to be called Big Data.
  • Variety indicates that Big Data technology works with many types of data, including text files, images, video, audio, etc. Moreover, it can fix “holes” in information packages through data fusion. 
  • Velocity denotes high speed of data processing and analysing. 

With the course of time, Big Data scientists added a few more V’s to the list. For example, Veracity that displayed the reliability of the data. Then came Viability and Value. They meant the importance of the data and in which way it may be used. All these principles are fair for Big Data and serves as main indicators for it. 

The Architecture of Big Data

Interrogation with Big Data requires special technologies and thus special architecture. Before appearing in the processed form, data goes through particular stages, performed by specific technologies:

  • Gathering. Data may come from different sources, like applications, websites, and so on. You can gather information from wherever you want. For this purpose you may choose Big Data solutions like Fluentd, Apache Kafka, Logstash, etc. 
  • Storage. After you get the needed information, you have to store it somewhere. In other words, you may need a lot of available space. There is a number of data storage systems that may come to rescue you. Just use HDFS, Cosmos DB, Amazon ElastiCache, and so on.
  • Analysis and processing. The main purpose of Big Data initiatives is analysing information and make relevant conclusion based on the results. Big Data analysis may include a number of different components, be it machine learning, neural networks, analytics platforms, artificial intelligence, and so on. Talking about technologies, you may consider Apache Spark, Hive, Amazon Kinesis, and so forth. 
  • Data Visualization. After you’ve gathered and processed all the information, you might want to have it in a comprehensible form. It is called Data Visualization. You may use much software providing it: Kibana, RStudio, Tableau, and so on.

What Other Features Does Big Data Have?

Apart from convenient architecture for processing huge arrays of information, Big Data has a number of other issues that make this interaction easier. Let’s examine them in detail:

  • Data Lake. Data Lake is a system that can store initial unstructured, semi-structured, or fully structured data. Usually it is a single store of all the records on a project. The major cloud computing platforms that provide Big Data services, have Data Lakes as of their components such as AWS Data Lake, Azure Data Lake, and Google Cloud Platform. 
  • Horizontal Scaling. This concept means the possibility to add new machines in your scope in order to increase the overall efficiency. Horizontal scaling is cheaper and more dynamic in comparison with vertical scaling (adding more power to the existing machine). It's all because it isn’t limited to the capabilities of a single machine.  
  • Fault Tolerance. It saves you from errors during data processing. When running a task, Big Data divides it into several loops with, let’s say, “checkpoints”. If something happens during processing, the task is restarted not from the very beginning, but from the nearest passed checkpoint, saving you a lot of time.
  • Data Safety. It is another feature of Big Data that reduces the risk of data loss. All your files are kept in three or more copies on different servers. You define its number. Thus, your data will be saved even if one server breaks down. 

In Conclusion

Modern customers are more fastidious than ever. It requires new tools for better interaction with them. Big Data gives these tools and takes communication to the next level. For now, you can perform highly targeted advertising campaigns knowing the visitors’ behavior scenarios and carry out more accurate marketing analyses. 

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