What Big Data Means and Why It Is Useful

Big Data is structured, semi-structured, or unstructured large data sets. In the article, we will talk about the characteristics and classification of Big Data and the methods of its processing and storage. We will also highlight big data management services and opportunities for working with Big Data.

Characteristics of Big Data

Despite the relevance for many areas, the boundaries of the term are blurred and may differ depending on the specific task. Nevertheless, there are three main features identified back in 2001 by the Meta Group.

They received the abbreviation VVV:

1. Volume. The volume of data is most often measured in terabytes, petabytes, and even exabytes. There is no exact understanding of how much data becomes “big”. There are tasks when information takes less than a terabyte, but due to the heterogeneous structure, their processing requires the power of a cluster of five servers.
2. Velocity. Growth rate and data processing. A striking example is that new data for analysis appears with each Facebook user session. Such information flows require high-speed processing. If one machine is enough to process data, it is not Big Data. The number of servers in a cluster is always greater than one.
3. Variety. Variety of data. Even if there is a lot of information, it has a clear and precise structure, this is not Big Data. Returning to the Facebook example, the biographies of social network users are structured and easy to analyze. But data on reactions to posts or time spent in the application does not have an exact structure.

Later, two other Vs were added:

1. Data viability. With a wide variety of data and variables, it is necessary to check their significance when building a forecasting model. For example, factors predicting a consumer’s propensity to buy: are product mentions in social networks, geolocation, product availability, time of day, and buyer profile.
2. Data value. After confirming viability, Big Data specialists study data relationships. For example, a service provider may try to reduce customer churn by analyzing the duration of calls to a call center. After estimating additional variables, the predictive model becomes more complex and efficient.

Big Data classification

1. Structured data. It is typically stored in relational databases. Organize data at the table level – for example, Excel. From the information that can be analyzed in Excel itself, Big Data differs in large volume.
2. Partially structured. The data is not suitable for tables but can be organized hierarchically. Text documents or files with records of events fit this description.
3. Unstructured. They do not have an organized structure: audio and video materials, photos, and other images.

Big Data sources

1. Human-generated social data, the main sources of which are social networks, the web, and GPS movement data. Also, Big Data specialists use statistical indicators of cities and countries: birth rate, death rate, the standard of living, and any other information that reflects the indicators of people’s lives.
2. Transactional information appears during any monetary transactions and interactions with ATMs: transfers, purchases, and deliveries.
3. Smartphones, IoT gadgets, cars and other equipment, sensors, tracking systems and satellites serve as a source of machine data.

How data is collected from the source

The initial stage (Data Cleaning) identifies, cleans, and corrects errors, irrelevant information, and data inconsistencies, as Digiteum explains. The process allows you to evaluate proxy indicators, errors, missing values, and deviations. Typically, data is transformed during retrieval. Big Data specialists add additional metadata, timestamps, or geolocation data.

There are two approaches to extracting structured data:

1. Full checkout where there is no need to track changes. The process is simpler, but the load on the system is higher.
2. Incremental extraction. Changes in the original data are tracked since the last successful retrieval. To do this, change tables are created or timestamps are checked. Many repositories have a built-in change data capture (CDC) feature that allows you to save data states. The logic for incremental retrieval is more complex, but the load on the system is reduced.

When working with unstructured data, most of the time will be spent preparing for extraction. The data is stripped of extra spaces and characters, duplicate results are removed, and how missing values ​​are handled.

Fuzzy matching techniques are essential for handling unstructured data in Big Data systems. These techniques enable flexible and approximate matching of textual data, even without an exact structure, facilitating the identification of patterns and similarities. By utilizing algorithms like Levenshtein distance or phonetic matching, organizations can effectively process and analyze unstructured data from various sources such as audio, video, and textual documents. These techniques enhance data analytics accuracy, providing valuable insights for informed decision-making, and empowering businesses to gain a competitive advantage in the data-driven landscape by unlocking hidden patterns and extracting actionable insights from unstructured Big Data sources.

Big Data storage approaches

For storage, data warehouses (Data Warehouse) or lakes (Data Lake) are usually organized. Data Warehouse uses the ETL principle (Extract, Transform, Load). First comes the extraction, then the transformation, then the load. Data Lake differs in the ELT (Extract, Load, Transform) method – first loading, then data transformation.

There are three main principles of Big Data storage:

1. Horizontal scaling. The system must be able to expand. If the amount of data has grown, you need to increase the capacity of the cluster by adding servers.
2. Fault tolerance. Processing requires large computing power, which increases the likelihood of failures. Big data must be processed continuously in real-time.
3. Locality. In clusters, the principle of data locality is applied – processing and storage take place on the same machine. This approach minimizes the power consumption for transferring information between servers.

Big Data management specialists

People working with big data are divided into many specialties:

• big data analyst;
• data engineer;
• data scientist;
• ML specialist, etc.

Given the high demand, specialists of different competencies are required to work in the field. For example, there is a direction of data storytelling – the ability to effectively convey information from a data set to the audience using storytelling and visualization. Storylines and characters, graphics and diagrams, images and videos are used to understand the context.

Data stories are used internally to inform employees about the need to improve the product based on the information provided. Another use is to present arguments to potential customers in favor of buying a product.

Final thoughts

Big data is used to develop IT products. For example, Netflix predicts consumer demand using predictive models for new online movie theater features. Streaming platform experts classify the key attributes of the popularity of films and series and analyze the commercial success of products and features. This is the key feature of such services.

In short, Big Data helps with semi-structured data about spare parts and equipment. Log entries and information from sensors can be indicators of an imminent breakdown. If it is predicted in time, it will increase the functionality, service life, and efficiency of equipment maintenance. And, Digiteum specialists are ready to provide you with high-quality Big Data management service.