Big Data processing encompasses the technologies, techniques, and processes used to analyse, process, and interpret vast amounts of data from a variety of sources. These processes make it possible to detect patterns and correlations and obtain information to support decision-making. Processing includes various phases, such as data collection, cleaning, analysis, and visualisation.
Big Data Processing
Type of technology
Description of the technology
Basic elements
- Hadoop/MapReduce: Frameworks for distributed computing.
- Apache Spark: Real-time data processing platform.
- NoSQL databases: Storage of unstructured data.
- Data processing languages: SQL, Python, R, Scala.
- Analytics platforms: Data visualisation and analysis tools such as Tableau and Power BI.
Industry usage
- Analysis of customer behaviour: Identification of purchasing patterns.
- Financial industry: Detecting anomalies and financial fraud.
- Production optimisation: Predicting the demand for raw materials and semi-finished products.
- Recommendation systems: Matching products to customer preferences.
- Real-time analysis: Process monitoring in industry and services.
Importance for the economy
Big Data processing enables companies to effectively use the collected information to optimise processes, improve product quality, forecast demand, and identify new market trends. Companies can make strategic decisions faster and adapt their operations to a rapidly changing market.
Related technologies
Mechanism of action
- The processing of large data sets is based on distributed processing algorithms that divide the data into smaller parts and then analyse it in parallel on multiple computing nodes. The results are merged into a single entity, which provides quick answers, even with huge volumes of data. Algorithms, such as MapReduce and Spark, enable real-time data analysis and predictive modelling.
Advantages
- Speed: Instant real-time data processing.
- Accuracy: Precise analysis, even with large volumes of data.
- Flexibility: Ability to adapt analysis methods to different types of data.
- Scalability: Processing data of different structure and size.
- Innovation: Detecting new patterns and trends.
Disadvantages
- High operating costs: The hardware and software requirements are expensive.
- Data quality issues: Risk of getting incorrect results due to data errors.
- Data security: Potential risks associated with unauthorised access.
- Process complexity: Processing large data sets requires advanced technical competence.
- Privacy issues: Risk of data protection breaches.
Implementation of the technology
Required resources
- Computing infrastructure: Data processing servers.
- Specialised software: Data processing tools, such as Apache Hadoop.
- Databases: Data storage and organisation systems, such as MongoDB and Cassandra.
- Analysis teams: Data processing and analysis specialists.
- Cybersecurity systems: Protection mechanisms for the processed data.
Required competences
- Data engineering: Big Data architecture design.
- Data analytics: Ability to process and interpret results.
- Programming: Knowledge of languages, such as Python, R, and Scala.
- Data management: Creation of ETL (Extract, Transform, Load) processes.
- Cybersecurity: Protecting processed data from threats.
Environmental aspects
- Energy consumption: High energy consumption of distributed computing systems.
- Waste generated: Problems with recycling decommissioned servers.
- Emissions of pollutants: Indirect emissions from the processing of large volumes of data.
- Raw material consumption: High wear of specialised electronic components.
- Recycling: Difficulties in recovering metals from advanced computing devices.
Legal conditions
- Data protection standards: Privacy regulations, such as GDPR.
- Data processing regulations: Controlling access to sensitive data.
- Intellectual property: Patents for Big Data processing technologies.
- Occupational safety: Regulations for data centre work.
- Export regulations: Export control of data processing technology.