Big Data infrastructure includes specialised hardware and software resources to store, process, and manage large amounts of data. It consists of servers, storage systems, network infrastructure, and computing platforms that enable high-speed processing and analysis of real-time data.
Infrastructure for Handling Big Data
Type of technology
Description of the technology
Basic elements
- • Computing servers: High-performance units for performing complex analytical operations.
- • Storage systems: Specialised systems for storing large data sets.
- • Network infrastructure: High-speed network connections to ensure the transfer of large amounts of data.
- • Cooling systems: Advanced cooling technologies in data centres.
- • Cloud platforms: Cloud computing services for scalable data management.
Industry usage
- Data centres: Storage and processing of data in large server rooms.
- Cloud systems: Data processing and analysis in distributed clouds.
- IoT data management: Storing and analysing data from numerous IoT devices.
- Financial analysis: Fast processing of market and transaction data.
- Monitoring systems: Real-time data processing for infrastructure health monitoring.
Importance for the economy
The right infrastructure to handle large data sets enables companies to store, process, and analyse data efficiently, resulting in better business decision-making, optimised operating costs, and the development of new services based on data analysis.
Related technologies
Mechanism of action
- A Big Data infrastructure is built around a combination of servers, storage and networks that allow huge amounts of data to be collected, stored, and transferred in a short period of time. The data is stored on servers or in the cloud and then processed by sophisticated algorithms and analysis applications.
Advantages
- Efficiency: Ability to process vast amounts of data in a short period of time.
- Scalability: Easy expansion of infrastructure with data growth.
- Flexibility: Ability to adapt resources to current needs.
- Cybersecurity: Advanced mechanisms to protect stored data.
- Savings: Optimisation of costs by using scalable cloud platforms.
Disadvantages
- High initial costs: Significant financial investment in equipment and infrastructure.
- Security challenges: Potential risk of data breaches.
- Management complexity: Requirement for high competence in infrastructure management.
- Dependence on providers: Reliance on the selected cloud or hardware provider.
- Compatibility issues: Difficulties in integrating different systems.
Implementation of the technology
Required resources
- Servers: High-performance computing units for data processing.
- Storage: NAS or SAN systems for storing large volumes of data.
- Network infrastructure: High-speed Ethernet, InfiniBand, or Fibre Channel connections.
- Specialised software: Data management and monitoring tools.
- Cloud platforms: Cloud services for scalable storage and processing.
Required competences
- Systems administration: Management and maintenance of servers and storage systems.
- Data engineering: Design and optimisation of large data sets.
- IT infrastructure management: Configuration and monitoring of complex IT systems.
- Cybersecurity: Protecting data and ensuring security in large sets.
- Cloud management: Ability to configure and manage cloud services.
Environmental aspects
- Energy consumption: High energy consumption in data centres.
- Waste generated: Electronic waste from decommissioned equipment.
- Emissions of pollutants: Indirect emissions from electricity consumption.
- Raw material consumption: High demand for metals and specialised components.
- Recycling: Problems with recycling complex electronic devices.
Legal conditions
- Data security: Data protection regulations, such as GDPR.
- Environmental standards: Energy consumption and emissions regulations.
- Export regulations: Export control of advanced information technology.
- Intellectual property: Patents for innovative data storage systems.
- Occupational safety: Regulations for safe data centre operations.