In this article, we explain what Big Data technology is in simple terms, how big data is processed, analyzed, and how it has changed the life of businesses in various industries.
The definition of the term Big Data is as follows: Big Data is large volumes of data for calculations and analysis that traditional processing systems cannot handle. With the help of special tools and algorithms, we can analyze these information flows, identify hidden patterns and predict the future.
For businesses, working with big data provides the following benefits:
- Accurate forecasts: Big data analysis allows you to predict future demand, potential problems, market dynamics and make the right decisions.
- Better understanding of customers: by analyzing information about purchases, preferences, and social media activity, you can create personalized offers and build long-term relationships with customers.
- Process optimization: By monitoring production indicators, logistics costs, and demand dynamics, you can eliminate bottlenecks, improve efficiency, and minimize risks.
- Competitive advantage: The technology called Big Data makes it possible to respond faster to market changes, identify new trends, develop innovative products and win over competitors.
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Main characteristics
What does the term “big data” mean? The main characteristics of the principle of big information are called 6 V, we list them below:
- Volume: Big Data is characterized by a large volume of information (from 150 GB per day), which cannot be processed by traditional methods. We are talking about terabytes, petabytes and even exabytes of information.
- Velocity: Big data is generated and fed into the system at incredible speed. The flow of information never stops and is constantly growing.
- Variety: The technology called Big Data includes information of different types: text, images, audio, video, sensory data and more.
- Veracity: Often contains noise, errors, and inaccuracies. It is important to be able to filter out bad data and identify real trends.
- Variability: Big Data is a dynamic world that is constantly changing, data can vary depending on time, location, context and other factors. It is important to be able to take this variability into account when analyzing information.
- Value: The key dimension of big data is its value. Extracting meaningful information from the total dataset allows you to make more informed decisions and achieve better results.
An important difference between big data and regular data is its distributed structure. Information is scattered across multiple servers, databases, and storages. Traditional data systems are unable to handle information of such scale and distribution. Therefore, special technologies are used to work with Big Data, which allow combining data from different sources, analyzing them in parallel, and identifying global trends.
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Varieties
Depending on the structure and organization, Big Data can be divided into three categories.
Structured Big Data
This is the most structured type of big data, which is stored in tables with rows and columns. Each element of big data has a specific type and place in the table. Examples of structured information: database data, Excel tables, CSV files. This can be information about sales, customers, inventory, financial transactions, transactions.
Structured big data is easy to analyze and process using traditional methods. Another advantage is that it is suitable for creating reports and analytical dashboards.
Semi-structured big data
This is data that is not always strictly systematized, but still contains some metadata that helps classify it. Examples: XML files, JSON files, emails, Word documents. This can be information about social media, news, blogging platforms, network interactions, search history.
The benefits of semi-structured big data include the combination of structure and flexibility, as well as the ability to analyze more complex data.
Unstructured Big Data
These are data without a specific structure, which are stored in an arbitrary format – texts, images, audio, video, sensor data and much more. For example, video from surveillance cameras, images from satellites, audio recordings, texts on social networks.
The benefits of unstructured big data: rich information and new opportunities for analytics.
Stages of work
Let us describe in turn the main blocks that make up working with big data.
Collection
First of all, let’s say about Big Data collection that it is the foundation for further analytics. Here are some key sources of Big Data collection:
- Social: a huge flow of information generated in social networks, blogs, etc. It reflects people’s opinions, feelings, interests, and behavior. Examples of such a source: posts on VKontakte, comments on Telegram, discussions on forums, reviews of goods and services.
- Statistical: official information collected by government agencies and other structures. It reflects macroeconomic indicators, demographic trends, social phenomena. Examples of such a source: data on population, birth rate, mortality, unemployment statistics, inflation, gross domestic product, data on trade, production, transport.
- Medical: information about people’s health collected in hospitals, clinics, and medical centers. It reflects diagnoses, treatments, medical histories. Examples of such a source: electronic medical records, test results, x-rays, data on medication intake, procedures, rehabilitation.
- Machine: information generated by various devices and systems. It reflects technical characteristics, system operation, engineering parameters. Examples of such a source: data from temperature, pressure, speed sensors, server log files, information on robot behavior.
- Transactional: information about financial transactions, purchases, sales and other transactions. It reflects consumer behavior, financial flows, market trends. Examples of such a source: data on credit cards, bank transfers, payments, information on sales of goods and services, online purchases.
It is important to add about Big Data collection that it requires cleaning (Data Cleaning) in the process — this means that only cleaned big data can serve as a reliable basis for further analytics. The process includes the following stages:
- Identification of incorrect data: search for errors, duplicates, omissions, inconsistencies.
- Error correction: replacing incorrect values with correct ones, removing duplicates.
- Filling in the blanks: filling in missing data using different methods.
- Big data transformation: converting data into a common format, such as converting numbers to text or vice versa.
- Standardization: Bringing big data to common standards, such as using the same units of measurement.
Storage
Traditional systems can no longer cope with large scales and diversity of information, so special tools are used for storage:
- DWH: a structured big data warehouse that provides a single view of business processes. Contains one version of data, which eliminates inconsistencies and confusion. Stores historical data, which allows you to record trends and dynamics of changes. Big data in DWH is organized into tables with rows and columns. The process of filling DWH is based on the ETL principle: Extraction, Transformation, Loading.
- Data Lake: An unstructured big data storage that can store any type of information in its native format. Data Lake provides flexibility and the ability to store large amounts of data. In addition to flexibility in formats, another clear advantage is the low cost of storage.
- DBMS (database management systems): these are software tools for structuring, storing and working with big data. DBMS can be structured (relational DBMS) or unstructured (NoSQL DBMS).
Choosing the right way to store Big Data depends on your specific needs and tasks. It is important to consider the type of data, storage volume, cost, and security requirements.
Processing
Big Data Processing Big Data is the process of extracting meaningful information from huge amounts of data. This requires special technologies for Big Data processing. One of the most popular approaches is MapReduce technology, which divides the task into two stages:
- Map: Big data is broken down into pieces that are processed independently.
- Reduce: The results are collected and combined into a single result.
Popular software that works with MapReduce:
- Hadoop: Open Source platform for storing and processing Big Data.
- Spark: A faster and more flexible platform than Hadoop.
Analysis
Big data is an object of analysis, i.e. the search for meaning, relationships, trends. At this stage, methods of statistical analysis, machine learning, text analysis, and big data visualization are used.
Among the tools for analysis we will name:
- SQL: A database query language that allows you to retrieve and process information.
- Neural networks: Machine learning algorithms that can analyze complex information and discover hidden relationships.
To extract the necessary layers of information and express them in an understandable format, services based on Business Intelligence (BI) are used, which provide the following capabilities:
- Information visualization: BI services allow you to visualize information in the form of graphs, charts, tables and other informative visualizations, making them more understandable and accessible for analysis.
- Interactive analysis: allows you to interactively analyze information, change filters, group information and discover new relationships between them.
- Create reports: You can create reports and dashboards that show key business metrics and help you make decisions.
- Integration with other systems: services can be integrated with different information storage systems (for example, CRM), which allows you to analyze information from different sources.
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