Big data refers to a collection of data that cannot be captured, managed, and processed with conventional software tools within a certain time frame. Big data requires a new processing model to have stronger decision-making power, insight and discovery, and process optimization capabilities. It is a massive, high-growth, and diversified information asset.
Big data is a large-scale data collection that greatly exceeds the capabilities of traditional database software tools in terms of the acquisition, storage, management, and analysis. Big data has four characteristics: massive data scale, fast data flow, diverse data types, and low-value density.
The strategic significance of big data technology is not to master huge data information, but to professionally process these meaningful data. In other words, if big data is compared to an industry, the key to profitability of this industry is to improve the "processing capability" of data, and to achieve the "value-added" of data through "processing".
From a technical point of view, the relationship between big data and cloud computing is as inseparable as the front and back of a coin. Big data cannot be processed by a single computer, it must adopt a distributed architecture. It is characterized by distributed data mining of massive data. But it must rely on the distributed processing of cloud computing, distributed database and cloud storage, and virtualization technology.
With the advent of the cloud era, big data has also attracted more and more attention.
The analyst team believes that Big data is usually used to describe a large amount of unstructured data and semi-structured data created by a company. These data will spend too much time and money when downloaded to a relational database for analysis. Big data analysis is often associated with cloud computing, because real-time analysis of large data sets requires a framework like MapReduce to distribute work to tens, hundreds, or even thousands of computers.
Big data requires special technology to effectively process a large amount of data within a tolerable elapsed time. Technologies applicable to big data include massively parallel processing databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.
The Big Data has the following features:
Volume: The volume of the data determines the value and potential information of the data under consideration;
Variety: Variety of data types;
Velocity: Velocity that data is obtained；
Variability: Hinder the process of processing and effectively managing data;
Veracity: Data quality;
Complexity: The amount of data is huge, and the sources are multi-channel;
Value: Use big data wisely to create high value at low cost;
Big data includes structured, semi-structured, and unstructured data. Unstructured data is increasingly becoming a major part of data. According to the IDC survey report: 80% of the data in the enterprise is unstructured data, and these data increase exponentially by 60% every year.
Big data is just a kind of appearance or characteristic of the Internet development to the present stage, there is no need to myth it or keep it in awe. Big data is set off against the backdrop of technological innovation represented by cloud computing, and these data that originally seemed difficult to collect and use are beginning to be easily utilized. Through continuous innovation in all walks of life, big data will gradually create more value for mankind.
It means that big data has become an important strategic resource that enterprises and society pay attention to, and it has become a new focus that everyone is vying for. Therefore, companies must formulate big data marketing strategic plans in advance to seize market opportunities.
Big data is inseparable from cloud processing, which provides flexible and expandable basic equipment for big data, and is one of the platforms for generating big data. Since 2013, big data technology has begun to be closely integrated with cloud computing technology, and it is expected that the relationship between the two will be closer in the future. In addition, emerging computing forms such as the Internet of Things and mobile Internet will help the big data revolution together, allowing big data marketing to exert greater influence.
With the rapid development of big data, big data is likely to be a new round of technological revolution, just like computers and the Internet. The subsequent emergence of related technologies such as data mining, machine learning, and artificial intelligence may change many algorithms and basic theories in the data world and achieve breakthroughs in science and technology.
In the future, data science will become a specialized subject, which will be recognized by more and more people. Major colleges and universities will set up special data science majors, and will also generate a number of new jobs related to them. At the same time, based on the basic platform of data, a cross-domain data sharing platform will also be established. Later, data sharing will extend to the enterprise level and become a core part of the future industry.
The growth rate of data breaches in the next few years may reach 100%, unless the data can be secured at its source. It can be said that in the future, every Fortune 500 company will face data attacks, regardless of whether they have taken security precautions. And all companies, regardless of size, need to re-examine today's definition of security. More than 50% of Fortune 500 companies will set up the position of Chief Information Security Officer. Enterprises need to ensure their own and customer data from a new perspective. All data needs to be secured at the beginning of its creation, not in the last link of data storage. Merely strengthening the latter’s security measures has proven to be useless.
Data management has become a core competitiveness and directly affects financial performance. When the concept of "data assets are the core assets of an enterprise" became popular, enterprises have a clearer definition of data management. Data management is regarded as the core competitiveness of the enterprise, sustainable development, strategic planning and use of data assets become enterprise data The core of management. The efficiency of data asset management is significantly positively correlated with the growth rate of main business income and sales revenue; in addition, for companies with Internet thinking, the competitiveness of data assets accounts for 36.8%, and the management effect of data assets will directly affect The financial performance of the business.
Companies that use self-service business intelligence tools for big data processing will stand out. One of the challenges is that many data sources will bring a lot of low-quality data. To be successful, companies need to understand the gap between raw data and data analysis, so as to eliminate low-quality data and get better decisions through BI.
The world of big data is not just a single, huge computer network, but an ecosystem composed of a large number of active components and multiple participant elements. Terminal equipment providers, infrastructure providers, network service providers, network access Into the ecosystem constructed by a series of participants such as service providers, data service enablers, data service providers, touch point services, data service retailers, etc. Today, the basic embryonic form of such a data ecosystem has been formed, and the next development will tend towards the segmentation of the internal roles of the system, that is, the segmentation of the market; the adjustment of system mechanisms, that is, the innovation of business models; Adjustment, that is, the adjustment of the competitive environment, etc., has gradually increased the degree of complexity of the data ecosystem.
The views expressed herein are for reference only and don't necessarily represent the official views of Alibaba Cloud.
Due to different emphases, traditional databases can be divided into the online transaction processing (OLTP) system and the online analytical processing (OLAP) system. With the development of the Internet, the data volume has increased exponentially, so standalone databases can no longer meet business needs. Especially in the analysis field, a single query may require the processing of a large part or full amount of data. It is urgent to relieve the pressure caused by massive data processing. This has contributed to the big data revolution based on Hadoop over the past decade, which solved the need for massive data analysis. Meanwhile, many distributed database products have emerged in the database field to cope with the increase in data volume in OLTP scenarios.
How should we design the architecture of a big data platform? Are there any good use cases for this architecture? This article studies the case of OpSmart Technology to elaborate on the business and data architecture of Internet of Things for enterprises, as well as considerations during the technology selection process.
How should we build the architecture of a big data platform? Are there any good use cases for this architecture? This article studies the case of OpSmart Technology to elaborate on the business and data architecture of the Internet of Things for enterprises, as well as considerations during the technology selection process.
Based on the "Internet + big data + airport" model, OpSmart Technology provides wireless network connectivity services on-the-go to 640 million users every year. As the business expanded, OpSmart technology faced the challenge of increasing amounts of data. To cope with this, OpSmart Technology took the lead to build an industry-leading big data platform in 2016 with Alibaba Cloud products.
Data powers intelligent business. As the market is maturing and enterprises are adopting various data analytics products and solutions, coherent data integration becomes a new challenge. Alibaba Cloud’s Data Analytics and AI solutions help you build a unified platform with full data analytic capabilities to streamline your data pipeline and create a consistent user experience throughout the complete data lifecycle. Alibaba Cloud provides industry solutions and applications to embed these data analytic capabilities into your business processes and professional Big Data Consulting Services to help lower total cost of ownership (TCO) and make your data analytics journey easier.
This certification is designed for those familiar with big data and operational knowledge of Alibaba Cloud big data products. ACP Big Data Certification covers a wide range of Alibaba Cloud big data core services, including MaxCompute, DataWorks, E-MapReduce, Visualization and BI tools.
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