In general, users may face a lot of market speculation to persuade you to migrate to more expensive systems. It may be that your current system is good enough - if it can be expanded, what the vendor offers to you may not be able to run well in your current environment.
SaaS application data face a TB-class growth rate; different SaaS application systems provide data structures that are not exactly the same, data is text, graphics, or even a small database; SaaS application data is distributed with the cloud service platform. It may be distributed on different servers. How to perform data mining on these heterogeneous and heterogeneous data is a problem faced by enterprises in the cloud era.
The Challenge of Enterprise Data Mining in the Cloud Age
Mining efficiency: After entering the era of cloud computing, BI's ideas have changed. Previously based on closed enterprise data mining, in the face of massive amounts of heterogeneous data after the introduction of Internet applications (it is estimated that by 2020, the amount of explosive growth data will exceed 35ZB (1ZB = 1 billion TB)), the current parallel The efficiency of mining algorithms is low.
Multi-source data: After cloud computing is introduced, the location of enterprise data may be on a platform that provides public cloud services, or on a private cloud built by enterprises. How to face different data sources for mining is also a challenge.
Heterogeneous data: The most prominent feature of Web data is semi-structured, such as documents, reports, web pages, sounds, images, videos, etc., while cloud computing brings a large number of SaaS applications based on the Internet model, how to sort out effective data is a challenge.
The data mining of SaaS application hopes to introduce rapid parallel mining algorithms through massive data storage platforms and improve the quality of data mining.
In general, users may face a lot of market speculation to persuade you to migrate to more expensive systems. It may be that your current system is good enough - if it can be expanded, what the vendor offers to you may not be able to run well in your current environment.
How to choose a reasonable infrastructure
For enterprises, how to dig out various application data and extract commercial information suitable for their use is an urgent need of the company. Traditional BI models are mostly based on data warehouses and are relational database models. Facing the rapid growth of heterogeneous data, traditional data warehouses and existing parallel computing technologies have been unable to solve the massive data mining work due to low mining efficiency, and have affected the timely extraction of data.
Historically, business intelligence systems are often built on the basis of traditional SMP architecture minicomputers. With the ever-increasing performance of X86 platforms, increasing availability, and rapid expansion in recent years, the X86 platform has begun to erode the share of minicomputers in more and more market segments. Business intelligence has also become another attack on the X86 architecture to RISC minicomputers. battlefield. For example, Oracle's Exadata Database Machine, based on the Intel Xeon platform, provides high OLAP performance (data warehousing applications) on the basis of the X86 architecture through the unique smartscan technology and the design of data processing process migration. And OLTP performance. In addition, IBM also introduced a business intelligence solution based on the X86 platform. Based on IBM's exclusive EX5 architecture server and XIV grid storage system, it provides intelligent information processing capabilities that are not lost on minicomputers.
Purchase points:
1, high availability: BI infrastructure layer, you need to establish a data mining cloud service platform, and this platform must be high availability.
From the perspective of high availability, we need to focus on solving three problems: First, data protection, the need to use the CRC, ECC and other hardware mechanisms to verify the data transmitted, error correction, if it can not be corrected, the damaged data will be Isolation to ensure that no larger data is created and system restarts and downtime are avoided.
Currently, the Intel Xeon 7500 or E7 cooperation solution has many advantages such as low cost, high performance, high reliability (RAS), and good scalability. In terms of scalable performance, the X86 platform's horizontal scaling capabilities consist of clusters of more than two machines. It can meet the load requirements of most enterprise critical application environments, including databases, business applications, and virtualization that require high memory and CPU requirements. In order to avoid the traditional UNIX two-machine program "high cost, standby machine resources are usually a serious waste of idle, user service was forced to pause during host failover" and many other difficulties.
In addition, some of the 7500 designs have minimized planning downtime, including system partition management techniques, hot addition of CPU and memory, and thermal removal to minimize system maintenance time.
2. Virtualization: Data mining Cloud services still rely on virtualization technology. It is necessary to allocate and schedule resources for computing. That is to say, virtualization technology is the support of data mining cloud service technology.
Never be fooled by the concept
There are many different uses of big data. Therefore, companies need to adopt different data mining platforms according to their own business conditions. For those customers who focus on application analysis and processing requirements, there are many specialized solutions, such as HP Vertica, as well as many high-performance NAS or target systems.
Similarly, consider HP Ibrix, Dell Exanet, BlueArc, HDS, NetApp, DataDirect Networks, Oracle7000, EMC Isilon, and VNX for video, security surveillance, CCTV, analog simulation, high bandwidth, or throughput.
In general, users may face a lot of market speculation to persuade you to migrate to more expensive systems. It may be that your current system is good enough - if it can be expanded, what the vendor offers to you may not be able to run well in your current environment.
For users, you need to be wary of all kinds of hype about big data. They may want to narrow down your options. In addition to the opportunities that big data can bring, there are many different aspects to consider, such as its characteristics, applications, usage examples, and deployment scenarios.
SaaS application data face a TB-class growth rate; different SaaS application systems provide data structures that are not exactly the same, data is text, graphics, or even a small database; SaaS application data is distributed with the cloud service platform. It may be distributed on different servers. How to perform data mining on these heterogeneous and heterogeneous data is a problem faced by enterprises in the cloud era.
The Challenge of Enterprise Data Mining in the Cloud Age
Mining efficiency: After entering the era of cloud computing, BI's ideas have changed. Previously based on closed enterprise data mining, in the face of massive amounts of heterogeneous data after the introduction of Internet applications (it is estimated that by 2020, the amount of explosive growth data will exceed 35ZB (1ZB = 1 billion TB)), the current parallel The efficiency of mining algorithms is low.
Multi-source data: After cloud computing is introduced, the location of enterprise data may be on a platform that provides public cloud services, or on a private cloud built by enterprises. How to face different data sources for mining is also a challenge.
Heterogeneous data: The most prominent feature of Web data is semi-structured, such as documents, reports, web pages, sounds, images, videos, etc., while cloud computing brings a large number of SaaS applications based on the Internet model, how to sort out effective data is a challenge.
The data mining of SaaS application hopes to introduce rapid parallel mining algorithms through massive data storage platforms and improve the quality of data mining.
In general, users may face a lot of market speculation to persuade you to migrate to more expensive systems. It may be that your current system is good enough - if it can be expanded, what the vendor offers to you may not be able to run well in your current environment.
How to choose a reasonable infrastructure
For enterprises, how to dig out various application data and extract commercial information suitable for their use is an urgent need of the company. Traditional BI models are mostly based on data warehouses and are relational database models. Facing the rapid growth of heterogeneous data, traditional data warehouses and existing parallel computing technologies have been unable to solve the massive data mining work due to low mining efficiency, and have affected the timely extraction of data.
Historically, business intelligence systems are often built on the basis of traditional SMP architecture minicomputers. With the ever-increasing performance of X86 platforms, increasing availability, and rapid expansion in recent years, the X86 platform has begun to erode the share of minicomputers in more and more market segments. Business intelligence has also become another attack on the X86 architecture to RISC minicomputers. battlefield. For example, Oracle's Exadata Database Machine, based on the Intel Xeon platform, provides high OLAP performance (data warehousing applications) on the basis of the X86 architecture through the unique smartscan technology and the design of data processing process migration. And OLTP performance. In addition, IBM also introduced a business intelligence solution based on the X86 platform. Based on IBM's exclusive EX5 architecture server and XIV grid storage system, it provides intelligent information processing capabilities that are not lost on minicomputers.
Purchase points:
1, high availability: BI infrastructure layer, you need to establish a data mining cloud service platform, and this platform must be high availability.
From the perspective of high availability, we need to focus on solving three problems: First, data protection, the need to use the CRC, ECC and other hardware mechanisms to verify the data transmitted, error correction, if it can not be corrected, the damaged data will be Isolation to ensure that no larger data is created and system restarts and downtime are avoided.
Currently, the Intel Xeon 7500 or E7 cooperation solution has many advantages such as low cost, high performance, high reliability (RAS), and good scalability. In terms of scalable performance, the X86 platform's horizontal scaling capabilities consist of clusters of more than two machines. It can meet the load requirements of most enterprise critical application environments, including databases, business applications, and virtualization that require high memory and CPU requirements. In order to avoid the traditional UNIX two-machine program "high cost, standby machine resources are usually a serious waste of idle, user service was forced to pause during host failover" and many other difficulties.
In addition, some of the 7500 designs have minimized planning downtime, including system partition management techniques, hot addition of CPU and memory, and thermal removal to minimize system maintenance time.
2. Virtualization: Data mining Cloud services still rely on virtualization technology. It is necessary to allocate and schedule resources for computing. That is to say, virtualization technology is the support of data mining cloud service technology.
Never be fooled by the concept
There are many different uses of big data. Therefore, companies need to adopt different data mining platforms according to their own business conditions. For those customers who focus on application analysis and processing requirements, there are many specialized solutions, such as HP Vertica, as well as many high-performance NAS or target systems.
Similarly, consider HP Ibrix, Dell Exanet, BlueArc, HDS, NetApp, DataDirect Networks, Oracle7000, EMC Isilon, and VNX for video, security surveillance, CCTV, analog simulation, high bandwidth, or throughput.
In general, users may face a lot of market speculation to persuade you to migrate to more expensive systems. It may be that your current system is good enough - if it can be expanded, what the vendor offers to you may not be able to run well in your current environment.
For users, you need to be wary of all kinds of hype about big data. They may want to narrow down your options. In addition to the opportunities that big data can bring, there are many different aspects to consider, such as its characteristics, applications, usage examples, and deployment scenarios.
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