Cloud data analytics systems are a set of tools and services that enable the processing, analysis, and visualisation of large data sets stored in cloud environments. They include data extraction, transformation and loading (ETL) tools, analytics systems, parallel processing engines, real-time analytics platforms, Artificial intelligence (AI) services, and machine learning (ML) services. With these systems, it is possible to gain insight into business data to support decision-making, predict trends, and optimise operations.
Cloud Data Analytics Systems
Type of technology
Description of the technology
Basic elements
- Analytical engines: Software for processing large volumes of data, such as Apache Spark and Hadoop.
- ETL systems: Data extraction, transformation, and loading tools, such as AWS Glue and Azure Data Factory.
- Cloud databases: Data storage solutions, such as Amazon RDS and Google BigQuery.
- Visualisation platforms: Reporting and data visualisation tools, such as Tableau and Power BI.
- AI and ML services: Systems supporting advanced data analysis using Artificial intelligence algorithms.
Industry usage
- Finance: Transaction data processing and risk analysis.
- E-commerce: Analysis of consumer behaviour and demand forecasting.
- Medicine: Processing of patient data and analysis of treatment effectiveness.
- Marketing: Customer segmentation and personalisation of marketing campaigns.
- Logistics: Route optimisation and real-time supply chain management.
Importance for the economy
Cloud-based data analytics systems support companies in optimising operations, predicting market trends, and making sound business decisions. They enable companies to quickly process and analyse large data sets, resulting in better management of resources, identification of new business opportunities, and better understanding of customer needs. As a result, the competitiveness of companies and their ability to respond quickly to market changes are increasing.
Related technologies
Mechanism of action
- Systems for analysing data in the cloud are based on the integration and processing of different data sources and visualisation of results. The process usually begins with extracting data from various sources, such as applications, databases, and files, and transforming it into a consistent format. The data is then loaded into data warehouses or analytics systems, where it goes through processing to extract information relevant to the user. The finished results are presented in the form of interactive reports, dashboards, or charts, which enables business users to easily view and analyse the data.
Advantages
- Scalability: Ability to process increasing amounts of data without sacrificing performance.
- Flexibility: Dynamic adjustment of resources to meet changing analytical needs.
- Processing speed: Real-time data processing and analysis.
- Integrated tools: Ability to use advanced analytical tools in a single environment.
- Availability: Ability to work with data from any location and device.
Disadvantages
- Data protection issues: Risk of data leaks and unauthorised access.
- High costs: Costs of storing and processing large volumes of data in the cloud.
- Technological complexity: Requirements for specialised knowledge and tools.
- Integration with various sources: Problems with combining data from different environments and formats.
- Dependence on providers: Risks of moving data between different cloud providers.
Implementation of the technology
Required resources
- Data warehouse systems: Tools for storing and processing large data sets.
- ETL systems: Software for data extraction, transformation, and loading.
- Analysis teams: Specialists in data analysis and machine learning.
- Cybersecurity systems: Tools to protect analytical data and control access.
- Data visualisation platforms: Tools for presenting analysis results in the form of reports and dashboards.
Required competences
- Data analysis: Ability to process and interpret large data sets.
- Programming: Knowledge of languages such as Python, R, and SQL.
- Data management: Ability to manage data in cloud environments.
- IT security: Protecting data from unauthorised access.
- Automation: Creation of scripts to automate data processing and analysis.
Environmental aspects
- Energy consumption: High energy consumption of servers processing large data sets.
- Emissions of pollutants: Emissions from the operation of advanced data centres.
- Raw material consumption: High demand for electronic components used to build servers.
- Recycling: Problems with recovering materials from analytical equipment.
- Waste generated: Electronic waste from IT equipment upgrades and replacements.
Legal conditions
- Data protection: Regulations for storing and processing personal data in the cloud, such as GDPR and CCPA.
- Safety regulations: Standards for securing data in cloud environments.
- Industry standards: Standards for quality and safety of data analysis.
- Intellectual property: Rights related to software and data analysis technologies.
- Compliance regulations: Regulations for compliance with local and international regulations.