Sergey Kondratenko: Big Data in finance

Fintech specialist Sergey Kondratenko believes that effective management of Big Data requires special solutions for storing and processing information. What they might be - the expert suggests figuring it out together.

A significant part of Big Data in finance comes from three main sources: machine, social and transactional data, reports Sergey Kondratenko. At the same time, it draws attention to the fact that companies also generate data internally through direct interaction with customers. This data is often stored on internal networks and firewalls. They can then be imported into management and analytics systems for more detailed analysis and use in business processes. So, the three main sources in Big Data are:

1. Machine data: are automatically generated from a variety of sources including sensors, SIEM logs, medical and wearable devices, IoT, cameras, satellites and many others. 
2. Social data:  data from social networks such as Facebook, Instagram, Twitter, YouTube, LinkedIn and others is collected through various user actions such as tweets, retweets, likes, video uploads and comments. Sergey Kondratenko notes that this rich data provides both qualitative and quantitative information about every aspect of the interaction between a brand and customers. Such data helps fintech businesses better understand their target market and customer base, which ultimately leads to more informed decisions.
3. Transactional data: represent information collected as a result of online and offline transactions. This data includes important details such as transaction date and time, location, items purchased, their prices, payment methods, discounts and coupons used, and other quantitative information. It is important to note that transactional data is the key source of business intelligence.

Having decided on the sources of Big Data, according to the expert, you need to sort out their storage. At the same time, provide a safe approach.

Sergey Kondratenko is a recognized specialist in a wide range of e-commerce services with experience for many years. Now, Sergey is the owner and leader of a group of companies engaged not only in different segments of e-commerce, but also successfully operating in different jurisdictions, represented on all continents of the world. The main goal is to drive new traffic, create and deliver an online experience that will endear users to the brand, and turn visitors into customers while maximizing overall profitability of the online business.

Sergey Kondratenko: What are Big Data storage systems and why are they needed?

In recent years, the need for storing and processing information has been growing rapidly. However, mega data has become available not only to large companies. Even small businesses accumulate significant amounts of information from email, social media interactions, sales, and other sources. Based on this, Sergey Kondratenko believes that regardless of the size of the company or its industry, in order to ensure the sorting and analysis of data, it must be stored in a warehouse.

The expert identifies the following options for Big Data storage systems:

• Data store is the process of collecting and managing information from various sources to create business information. 
• Data Lake is a central repository that collects vast amounts of data from various sources in raw form. 
• Network Attached Storage (NAS) is a storage device that is accessible over a network rather than directly from a computer. 
• Cloud represents one of the most popular ways to store large amounts of data in the modern fintech world. Sergey Kondratenko says if you have used, for example, iCloud or Google Drive, then this means that you have already tried cloud storage for your documents and files. 
• Object storage is a data storage method that treats information as objects. All data is stored in a single storage that can be shared across multiple physical devices, rather than being split into files and folders.

- Object storage systems include blocks of data that form files or “objects” and their metadata. Additional metadata is added to each object to provide access to the data without the need for hierarchy. All objects are located in the same address space, and to search for them, users enter a unique identifier, explains Sergey Kondratenko.

Sergey Kondratenko: Architecture and tools for secure storage of Big Data

Data protection is another very important aspect for any fintech company. There is often an incorrect assumption that data within an organization is private and secure. However, cyber attacks and hacks have become commonplace in today's high-tech world. Given this situation, Sergey Kondratenko believes that any financial organization should be able to manage data security. In this context, provision is made to ensure the security of business information and prevent unauthorized access. The expert suggests that fintech companies pay special attention to this Big Data security architecture:

1. Data classification. 
2. Encryption of confidential data. 
3. Data storage using ORAM technology. 
4. Accessing data using path hiding. 

Gradually, more and more financial processes are moving into digital form, and the risks associated with data security are also growing. According to Sergey Kondratenko, leaks and security breaches can occur at different stages of information processing, from its collection to storage, analysis and processing. Therefore, there was a need to develop and implement best practices and strategies to ensure strong data security, especially in the fintech space where transactions take place 24/7.