Systems for processing and storing big include complex technological solutions for collecting, storing, processing, and sharing huge amounts of data in real time. They consist of specialised servers, storage systems, distributed databases, and parallel processing software to efficiently handle and analyse data from different sources.
Systems for Processing and Storing Big Data
Type of technology
Description of the technology
Basic elements
- NoSQL databases: Storing unstructured data in distributed systems.
- HDFS (Hadoop Distributed File System): A file system for storing large volumes of data.
- Cloud storage: Storing data in a public or hybrid cloud.
- Server systems: High-performance computing and storage units.
- Analytics platforms: Real-time data processing and analysis tools.
Industry usage
- Cloud systems: Data storage and processing in public and private clouds.
- Data centres: Large-scale data processing in dedicated data centres.
- IoT data analytics: Real-time processing of data from IoT devices.
- Recommendation systems: Storing customer data to personalise services.
- Energy industry: Power grid data monitoring and management.
Importance for the economy
These systems enable companies to effectively manage data resources, leading to better use of information, optimisation of operating costs, and the creation of new data-driven business models. With these systems, organisations can store and process structured data to support decision-making processes and the development of innovative digital services.
Related technologies
Mechanism of action
- Systems for processing and storing large data sets operate on the basis of distributed databases and file systems that enable simultaneous storage, reading, and writing of data. They use a cluster architecture in which multiple servers operate as a single system, which enables efficient processing and fast access to data. Parallel processing of data by distributed computing nodes enables real-time dynamic analysis.
Advantages
- Scalability: Ability to expand the infrastructure according to data growth.
- Efficiency: Fast processing and storage of huge volumes of data.
- Flexibility: Ability to integrate different types of data into a single system.
- Cybersecurity: Advanced mechanisms to protect data from loss and attacks.
- Reliability: High availability due to cluster architecture.
Disadvantages
- High implementation costs: Significant costs purchase and maintenance of infrastructure.
- Management complexity: Difficulties in monitoring and optimising distributed systems.
- Data security: Risk of data breaches with a large number of access points.
- Compatibility issues: Difficulties in integrating different systems and technologies.
- Failure rate: Possibility of storage systems failures leading to data loss.
Implementation of the technology
Required resources
- Computing servers, Databases, Analytics software, Specialised infrastructure, Cloud platforms Computing servers: High-performance computing units.
- Databases: Systems for storing large amounts of data, such as Cassandra and MongoDB.
- Analytics software: Data analysis tools, such as Apache Spark.
- Specialised infrastructure: Cooling and power distribution systems in data centres.
- Cloud platforms: Cloud storage and processing services.
Required competences
- Data engineering, Systems administration, IT infrastructure management, Cybersecurity, Data engineering: Design and management of data storage systems.
- Systems administration: Maintaining and optimising Big Data systems.
- IT infrastructure management: Configuration and monitoring of distributed systems.
- Cybersecurity: Protecting data systems from threats.
- Data analytics: Processing and interpretation of analysis results. Data analytics
Environmental aspects
- Energy consumption, Waste generated, Emissions of pollutants, Consumption of raw materials, Recycling Energy consumption: High electricity demand in data centres.
- Waste generated: Problems with recycling decommissioned computing equipment.
- Emissions of pollutants: Emissions from high electricity consumption.
- Raw material consumption: High consumption of metals and electronic materials.
- Recycling: Difficulties in recovering materials from complex computing devices.
Legal conditions
- Data security: Regulations for the protection of sensitive data.
- Data processing regulations: Data storage and analysis requirements.
- Intellectual property: Patents for data storage and processing technologies.
- Environmental standards: Energy consumption and emissions regulations.
- Export regulations: Export control of data processing technology.