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Big data OLAP blockchain
Publish: 2021-04-18 02:16:59
1. It's not the same thing
big data refers to the technical difficulties caused by too much data, mainly 4V, large amount of data, fast data generation, diverse data formats and low data value
there are many corresponding technical problems to be solved
blockchain is a technical term in the field of information technology. In essence, it is a shared database. The data or information stored in it is characterized by "unforgeability", "trace in the whole process", "traceability", "openness and transparency" and "collective maintenance".
big data refers to the technical difficulties caused by too much data, mainly 4V, large amount of data, fast data generation, diverse data formats and low data value
there are many corresponding technical problems to be solved
blockchain is a technical term in the field of information technology. In essence, it is a shared database. The data or information stored in it is characterized by "unforgeability", "trace in the whole process", "traceability", "openness and transparency" and "collective maintenance".
2. The decentralized big data transaction realized on the blockchain can effectively rece the contacts of the original data and fundamentally guarantee the security of the data
in the future, jinwowo will focus on blockchain technology to promote the legal circulation and commercial application of big data.
in the future, jinwowo will focus on blockchain technology to promote the legal circulation and commercial application of big data.
3. The relationship between Chongqing jinwowo analysis blockchain technology and big data is as follows:
the relationship between blockchain and big data is not very big. The main purpose of big data is to manage massive data, and the core of blockchain is to achieve high security and high reliability of data without centralized intermediary accounting
therefore, blockchain and big data do not conflict with or replace each other. They are completely different solutions for data in different scenarios.
the relationship between blockchain and big data is not very big. The main purpose of big data is to manage massive data, and the core of blockchain is to achieve high security and high reliability of data without centralized intermediary accounting
therefore, blockchain and big data do not conflict with or replace each other. They are completely different solutions for data in different scenarios.
4. The relationship between jinwowo analysis blockchain technology and big data is as follows:
the relationship between blockchain and big data is not very big. The main purpose of big data is to manage massive data, and the core of blockchain is to achieve high security and high reliability of data without centralized intermediary accounting
therefore, blockchain and big data do not conflict with or replace each other. They are completely different solutions for data in different scenarios.
the relationship between blockchain and big data is not very big. The main purpose of big data is to manage massive data, and the core of blockchain is to achieve high security and high reliability of data without centralized intermediary accounting
therefore, blockchain and big data do not conflict with or replace each other. They are completely different solutions for data in different scenarios.
5. Chongqing jinwowo analysis: with its trustworthiness, security and non tamperability, blockchain liberates more data and promotes the massive growth of data.
6. Blockchain and big data are hot topics. The development of big data is earlier than that of blockchain. At present, it has become a huge instry. The combination of developing blockchain technology and big data will bring about different effects. From a technical point of view, big data technology exchanges computing resources with trust, while blockchain technology exchanges computing resources with trust, so the combination of the two sets off a new wave of information security. Based on the characteristics of blockchain distributed data storage, decentralization, non tampering, traceability and trustworthiness, Chongqing jinwowo network technology group has established a strong blockchain research team to provide big data services with blockchain as the underlying technology.
7. Big data is a huge data group collected from many sources in multiple forms, often with real-time. In the case of business to business sales, the data may come from social networks, e-commerce websites, customer visit records, and many other sources. These data are not the normal data group of the company's customer relationship management database. From a technical point of view, the relationship between big data and cloud computing is as inseparable as the positive and negative sides of a coin. Big data can not be processed by a single computer, so it must adopt distributed computing architecture. It is characterized by massive data mining, but it must rely on cloud computing distributed processing, distributed database, cloud storage and / or virtualization technology. The meaning of big data is accompanied by the increasingly popular network behavior of human beings. It is collected by relevant departments and enterprises, and contains the real intention and preference of data procers. It is non-traditional structure and meaning of data.
8. If you don't understand this, don't do it. It's all virtual things. When the landlord asks questions, it's deceptive. It's obvious that those who advertise on this platform value your principal
9. We have always heard of data mining, OLAP, data statistics and other professional words in the field of big data. But many people don't understand these words very well. In this article, we will introce some knowledge about data mining, big data, OLAP and data statistics, in order to help you understand these technologies preliminarily< The level of data analysis
data analysis is a big concept. In theory, any process of calculating and processing data to draw some meaningful conclusions is called data analysis. From the complexity of data itself, as well as the complexity and depth of data processing, data analysis can be divided into four levels, namely data statistics, OLAP, data mining, big data
2. Data statistics
data statistics is the most basic and traditional data analysis, which has existed since ancient times. It refers to sorting, screening, operation, statistics and other processing of data through statistical methods, so as to draw some meaningful conclusions
3. OLAP
OLAP is on-line analytical processing (OLAP), which refers to online multidimensional statistical analysis based on data warehouse. It allows users to observe a measure from multiple dimensions online, thus providing support for decision-making. OLAP further tells you what will happen next, and what will happen if I take such measures
4. Data mining
data mining refers to finding unknown, potentially useful and hidden rules from massive data. It can find some deep-seated reasons that can not be obtained by observing charts through association analysis, cluster analysis, time series analysis and other algorithms. In view of this, we can take targeted management measures
5. Big data
big data is a large-scale data set that is difficult to collect, store, manage, analyze and use with existing computer software and hardware facilities. Big data has the characteristics of large scale, variety, rapidity and low value density. The "big" of big data is a relative concept, and there is no specific standard. If a standard must be given, then 10-100tb is usually called the threshold of big data
it can be seen that from the perspective of data analysis, the data application procts of most schools are still in the stage of data statistics and report analysis, few can achieve effective OLAP analysis and data mining, and very few can reach the stage of big data application, at least have not used effective big data sets
this is a brief introction to data mining, big data, OLAP and data statistics. In fact, these knowledge are not as simple as we said. We need to really understand these knowledge to better understand data analysis and master data analysis.
data analysis is a big concept. In theory, any process of calculating and processing data to draw some meaningful conclusions is called data analysis. From the complexity of data itself, as well as the complexity and depth of data processing, data analysis can be divided into four levels, namely data statistics, OLAP, data mining, big data
2. Data statistics
data statistics is the most basic and traditional data analysis, which has existed since ancient times. It refers to sorting, screening, operation, statistics and other processing of data through statistical methods, so as to draw some meaningful conclusions
3. OLAP
OLAP is on-line analytical processing (OLAP), which refers to online multidimensional statistical analysis based on data warehouse. It allows users to observe a measure from multiple dimensions online, thus providing support for decision-making. OLAP further tells you what will happen next, and what will happen if I take such measures
4. Data mining
data mining refers to finding unknown, potentially useful and hidden rules from massive data. It can find some deep-seated reasons that can not be obtained by observing charts through association analysis, cluster analysis, time series analysis and other algorithms. In view of this, we can take targeted management measures
5. Big data
big data is a large-scale data set that is difficult to collect, store, manage, analyze and use with existing computer software and hardware facilities. Big data has the characteristics of large scale, variety, rapidity and low value density. The "big" of big data is a relative concept, and there is no specific standard. If a standard must be given, then 10-100tb is usually called the threshold of big data
it can be seen that from the perspective of data analysis, the data application procts of most schools are still in the stage of data statistics and report analysis, few can achieve effective OLAP analysis and data mining, and very few can reach the stage of big data application, at least have not used effective big data sets
this is a brief introction to data mining, big data, OLAP and data statistics. In fact, these knowledge are not as simple as we said. We need to really understand these knowledge to better understand data analysis and master data analysis.
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