Big data and cloud computing complement each other
Big data and cloud computing complement each other
The value of big data has begun to receive increasing attention, and people's requirements for real-time and effectiveness of data processing are also increasing. Nowadays, the application of big data is not limited to the field of BI (Business Intelligence). Big data is also exerting a huge influence in public services, scientific research and other aspects, and the application scope is much wider. . For example, the National Oceanic and Atmospheric Administration in the United States is trying to use big data methods to help conduct research on climate, ecosystems, weather, and business. Google Flu Trends uses aggregated Google search data to estimate influenza epidemics. Data has undoubtedly become an increasingly important resource in the information society.
The significance of big data does not lie in its characteristics such as large capacity and diversity, but in how we manage and analyze the data, and the value we discover as a result. If there is a lack of corresponding technical support in analysis and processing, the value of big data will be impossible to discuss.
As far as enterprises are concerned, the business decision-making process in the era of big data has obvious data-driven characteristics. This characteristic brings massive amounts of historical data to be processed and complex information to the enterprise's IT system. Challenges include mathematical statistics and analytical models, strong correlations between data, and re-evaluation caused by frequent data updates. This requires the underlying data support platform to have strong communication (data flow and exchange) capabilities, storage (data retention) capabilities, and computing (data processing) capabilities to ensure massive user access, efficient data collection and processing, and multi-mode Accurate real-time sharing of data and rapid response to changes in demand.
Traditional processing and analysis technologies are beginning to encounter bottlenecks in the face of these demands. The emergence of cloud computing not only provides us with a tool to mine the value of big data and highlight it, but also makes big data more valuable. Applications have more possibilities.
Cloud computing includes two aspects: service and platform, so cloud computing is both a business model and a computing model. For example, the University of California, Berkeley, in a report on cloud computing, believes that cloud computing refers to applications provided as services on the Internet. Also refers to the hardware and software that provide these services in the data center.
From the current technological development point of view, cloud computing is data-centric, uses virtualization technology as a means to integrate various resources including servers, storage, networks, applications, etc., and uses SOA architecture to provide users with Provides various application data services that are safe, reliable and convenient; it completes the process of system architecture moving from components to levels and then to resource pools, realizing the "universal" level of different platforms (hardware, systems and applications) of the IT system, breaking the physical Equipment barriers to achieve centralized management, dynamic deployment and on-demand use.
With the help of the power of "cloud", we can realize the unified management, efficient circulation and real-time analysis of multi-format and multi-mode big data, explore the value of big data and give full play to the true meaning of big data.
Big data places high demands on technology
Big data processing is first to obtain and record data; secondly to complete data extraction, cleaning and annotation as well as data integration, aggregation and expression, etc. Important preprocessing or processing (depending on the actual problem) work; again a complete data analysis step is required, usually including data filtering, data summarization, data classification or clustering and other preprocessing steps, and finally enters the analysis stage. At this stage, Various algorithms and computational tools are applied to the data in order to get results that the analyst wants to see or can interpret.
Involving a huge amount of data, this entire set of processing processes will pose challenges to traditional technical methods at various stages. For example, massive networked devices, massive online users, and uninterrupted network connections generate large amounts of multi-format content data and status information at all times, which are transmitted through various clients (web pages, applications, sensors, etc.) The collected information data, together with thousands of access and operation requests, will put pressure on the system server in a highly concurrent manner.
Usually in order to avoid the problem of queuing service requests due to insufficient service capabilities, load balancing technology will be used to share the pressure on a single server and greatly improve service performance; during data collection, it will also be used Deploy a large number of databases on the collection end to support system performance, and then perform data cleaning, deduplication, regularization and corresponding formatting on the collected data (including various structured, unstructured and semi-structured data, etc.) Conversion processing. After filtering according to predetermined rules, it is output to a distributed data storage system for storage to prepare for subsequent analysis and display.
In the analysis stage, in order to complete the purpose of data mining, it is usually necessary to process massive historical data and construct complex mathematical statistics and analysis models (such as calculating the impact of winter temperature levels on the sales of down jackets of a specific thickness). , and make efficient and correct processing of the correlation between a large number of results, while also supporting re-evaluation caused by data updates; in the display stage, implementation details such as data storage topology and data storage structure should be hidden, Expose standardized data access interfaces to business applications, provide transparent support for complex data access requirements, and greatly reduce the difficulty of building business applications.
These complex requirements place high demands on technical implementation and underlying computing resources.
Therefore, in order to deal with these complex big data processing tasks, it is necessary to build a system environment with both high availability and high reliability from all aspects of server, network, storage, software, etc., to provide an end-to-end comprehensive solution.
Big data and cloud computing complement each other
The traditional stand-alone processing model is not only more and more expensive, but also difficult to expand. As the amount of data increases, the complexity of data processing increases. , the corresponding performance and expansion bottlenecks will become larger and larger. In this case, the basic elements of cloud computing such as elastic scaling and dynamic allocation, resource virtualization and system transparency, support for multi-tenancy, support for pay-as-you-go or on-demand usage, and green energy saving are just right. The demand for new big data processing technology; and the new generation of computing model represented by cloud computing, as well as the underlying infrastructure of cloud computing platform that supports all upper-layer application services, with its high reliability, stronger processing capabilities and more Features such as large storage space, smooth migration, elastic scalability, transparency to users, and unified management and scheduling are becoming important directions for future computing technology development to solve big data problems.
The big data platform built based on cloud computing technology can provide discrete communication, storage and processing capabilities in aggregate large-scale distributed systems, and provide them to upper-layer platforms and applications in a flexible, reliable and transparent form. . It also provides cross-system, cross-platform, and cross-application unified management methods for massive multi-format and multi-mode data and a highly available and agile response mechanism system to support rapidly changing functional goals, system environments, and application configurations.
For example, in the new enterprise information system built based on cloud computing platform, after building a high-performance and high-scalability storage platform with distributed cluster technology, we can realize the processing of different formats and formats in different business applications. Unified storage of massive data with different access modes, and related data analysis systems are built on the framework of distributed workflow and scheduling systems, using distributed computing methods to provide data conversion, association, extraction, aggregation and processing for multi-mode massive data. Data mining and other functions. The specific business functions of BI often mentioned in enterprise information systems, such as decision support, sales forecasting, etc., can be realized by upper-layer business applications by calling the functions provided by the data analysis system and adding business logic.
Cloud computing makes big data applications possible; without the emergence of cloud computing, big data would still be a castle in the air, lacking foundation and implementation possibilities. With the help of cloud computing technology, the overall elasticity and flexibility of the system can be improved, management costs and risks can be reduced, and the availability and reliability of application services can be improved. Cloud computing not only creates an efficient and reliable system environment for big data processing, but also gives full play to The advantages of cloud computing platforms can find more diversified outlets for big data applications.
If big data is a mine containing huge value, cloud computing can be regarded as a powerful tool for mining operations; without the processing power of cloud computing, no matter how rich the information in big data is, it may not be We can only look at the ocean and sigh, entering the treasure mountain and returning empty-handed; but from another perspective, cloud computing is also a technological trend developed to solve "big" problems such as big data. Without the information precipitation of big data, the functions of cloud computing will be ineffective. to full play. Therefore, on the whole, big data and cloud computing are complementary to each other.