Which of the following options is currently not supported effectively using big data analytics techn

Summary. Big data is made up of huge data sets that are often larger than humans can collect, use, manage, and process in an acceptable amount of time. The data can be aggregated and analyzed to derive a lot of additional information and data relationships, which can be used to detect business trends, determine the quality of research, avoid the spread of disease, fight crime, and measure real-time traffic conditions. This is why large datasets are prevalent.

Big data consists of giant datasets that often exceed the capacity of humans to collect, use, manage, and process fiber collection and sales in an acceptable amount of time. The data can be aggregated and analyzed to derive a lot of additional information and data relationships, which can be used to detect business trends, determine the quality of research, avoid the spread of disease, fight crime, and measure real-time traffic conditions. This kind of use is why large data sets are prevalent.

Big data refers to the data set chain that cannot be captured, managed, and processed by conventional software tools in a certain period of time, and is a massive, high-growth and diversified information asset that requires new processing modes to have stronger decision-making, insight and process optimization capabilities.

Answer] :d The most important difference between big data analysis and traditional data analysis is the amount of data, option d is wrong.

Answer]: The key technologies of big data include: big data storage management technology, big data parallel analysis technology and big data analysis technology.

Regarding the description of big data, the following is incorrect.

a.There are a lot of them.

b.The composition is complex.

c.Static. d.Change quickly take a reed.

The answer is c

The new technologies that need to rely on the mountain shelter for big data applications are ().

a.Storage and compute at scale.

b.Data analysis and processing.

c.Wisdom can turn the state into an empty grandson.

d.All other options are.

Correct answer: d

Answer] :d big data requires special techniques to effectively handle large amounts of tolerated elapsed data gaps. Qingchang is suitable for big data technologies, including massively parallel processing (MPP) databases, data mining, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.

Answers]: a, b, c, d

Traditional data analysis is "lead-key backward analysis", which analyzes what has already happened. In the era of big data, data analysis is "forward analysis", which is the best nature. Traditional data analysis focuses on structured data.

Big data not only includes the traditional structured data based on text materials, but also includes all semi-structured and unstructured data such as text, audio, and other semi-structured and unstructured data in the information age, and is dominated by semi-structured and unstructured data. Big data analysis is based on massive raw data, and there is no need to set research objectives and methods in advance, but to find the relationship between data and establish a model from a large amount of data through data mining technology, find the root cause of the actual situation, and even form theories and new cognitions, and on this basis, the future will be optimized and optimized to achieve continuous improvement and innovation in various fields of social operation, and options a, b, c, and d are correct.

Closely related to big data is:

1. Data processing technology

In big data processing technology, when the flowing data enters the memory, the data is directly calculated and analyzed in real time, and more attention is paid to the timeliness of the data and the interaction of users. The real-time stream computing process of data not only needs to be completed without data landing, but also needs to consider various complex factors such as multi-stream merging, multi-stream association with external dimension tables, abnormal time windows, and other business function operations, which requires higher system performance than batch processing.

2. Data storage technology

The storage and computing of big data are complementary to each other, and storage is the focus of business data and the basis of data processing and analysis. In order to reduce the storage of redundant data, the paradigm pattern of data should be envied as much as possible. There are many types of big data and are widely distributed, and when complex query operations can be associated in multiple data tables, groupby or orderby operations are frequently used, which significantly reduces query performance and affects user experience.

In addition, there are problems such as low space utilization rate and troublesome shutdown operation in the adjustment of data structure. Distributed storage distributes data across multiple inexpensive servers instead of expensive dedicated storage hardware, and uses a copy mechanism for data storage to solve the problem of data loss and improve data reliability.

3. Kafka technology

Big data, high-performance computing capabilities, and deep neural network architectures are the core elements and components of deep learning. Kafka classifies and stores messages based on different topics, and each topic consists of multiple partitons. Kafka is the preferred framework for intermediate messaging due to its durability, reliability, and high concurrency.

The departure of producers and consumers in Kafka does not have any additional impact on the Kafka cluster.

What big data is

Big data, or big data, refers to massive, high-growth, and diverse information assets that require new processing models to have stronger decision-making, insight, and process optimization capabilities.

What is cloud computing

Cloud computing is an Internet-based model for the addition, use, and delivery of related services, which provides available, convenient, on-demand network access to a shared pool of configurable computing resources, which can be provided quickly with little management effort or interaction with service providers. Cloud is a metaphor for the network, the Internet.