Cloud Data Warehouse Architecture

Intermediary transaction SEO diagnosis Taobao guest Cloud host technical Hall The purpose of the data Warehouse is to build an integrated data environment for analysis, and to provide decision support for enterprises (Decision Support). In fact, the data warehouse itself does not "produce" any data, at the same time does not need to "consume" any data, data from the outside, and open to external applications, which is why called "Warehouse", not called "factory" reasons. Therefore, the basic structure of the data warehouse mainly contains the data inflow and outflow process, can be divided into three layers-source data ...

The intermediary transaction SEO diagnoses Taobao guest stationmaster buys cloud host technology Hall the article before--the website data analysis Some questions 2 mainly collates the bi related question, this article mainly wants to organize some data warehouse related question. Because I recently looked back at some data warehouse information and books, want to put forward as well as the current problems to come up (blog about data Warehouse related content Please refer to the website Data Warehouse this directory), at the same time they also have the knowledge of the data warehouse under the reorganization and understanding, and for a long time ...

I think the data Warehouse can help the enterprise to deal with the data problem in three ways: first, in an enterprise Data warehouse, you divide your data according to the subject area, which is often more stable. 　　Organizations that want to understand the "big data" concept need to make a choice between the traditional data warehouse concept and the existing Data Warehouse architecture, or the increasingly popular open source Hadoop distributed processing platform, or the combination of the two. Those who want to move from simple BI reports to deep data mining and predictive analytics ...

The traditional business intelligence system only serves for the decision person, and is affected by the security factor, the analysis data can only be reflected in the local user terminal, but manifests the way almost is the ordinary computer. With the rapid development of computing and Internet technology, the mobility and activity space of sales, market and decision makers are more and more, more and more managers involved in decision-making, the real-time and interactive requirements of information transmission are more and more high, which puts higher demands on business intelligence. The development of the next generation business intelligence requires that it not only can provide strategic decision for senior decision-makers, but also can ...

Recently, the world-renowned 12th annual Teradata Data Warehouse and Enterprise Analysis Summit held in Suzhou, chapter of "The ultimate interpretation of the value of data" as the theme, in-depth discussion of how enterprises to fully utilize the latest data warehouse and enterprise analysis technology, with the help of data sources to provide insight to quickly formulate the best business decisions, grasp the business opportunities. At the press conference, Teradata dynamic Enterprise Data Warehouse platform 6690 (Active EDW 6690) platform began to supply, provide the latest generation of hybrid storage, further enhance the capacity of data warehousing, help ...

Dong Xin: Ladies and gentlemen, good afternoon, welcome to the Hello Cloud architecture of the sub-forum, I am sure that many of you have participated in the morning meeting this afternoon in the Cloud Architecture forum, where they sat together to discuss how to put the cloud's vision, how to put the big data to the final landing, how to make our calculation , our storage, our network, can be better integrated and played in the era of cloud application architecture change. So we are also very happy today to invite experts in the industry, corporate executives, including our customers and our partners, so here I also represent super ...

Large data will challenge the enterprise's storage architecture and data center infrastructure, and will trigger the ripple effect of cloud computing, data Warehouse, data mining, business intelligence and so on. In 2011, companies will use more TB (1TB=1000GB) Datasets for business intelligence and Business Analytics, and by 2020 global data usage is expected to rise 44 times-fold to 35.2ZB (1zb=10 billion TB). The challenges of large data for the vast number of data information, how the complex application of these data into the current data warehousing, business intelligence and data analysis technology ...

The big data age is putting pressure on data storage, and more and more organizations need to build a storage platform that can store large amounts of structured and unstructured data on the basis of cloud computing technology. 　　Cloud storage should be "cloud"-born. The so-called cloud storage, refers to through the cluster application, grid technology or Distributed file system functions, the network of a large number of different types of storage devices through the application software together to work together to provide data storage and business access functions of a system. The key to implementing cloud storage that adapts to user needs is to choose a strong data architecture based on application and deployment requirements ...

This method allows the architect to complete the build locally to provide the expected workload and overflow to the on demand cloud HPC to cope with the peak load. Part 1th focuses on how system builders and HPC application developers can extend your systems and applications most efficiently. Processor cores with custom extensions and shared memory the external HPC architecture of the internet is rapidly being replaced by on-demand clustering, which leverages off-the-shelf general-purpose vector collaboration processors, converged Ethernet (each link Gbit or higher), and multicore headless ...

Apache Hadoop is the foundation of a new generation of data warehouses. Hadoop is used by companies as a strategic role in their current warehousing architectures, such as extraction/transformation/loading (ETL), data staging, and unstructured content preprocessing. I also see Hadoop as a key technology in a new generation of large-scale parallel data warehouses in the cloud, and Hadoop complements today's warehousing techniques and low latency streaming platforms. At IBM, we look forward to the next few years, Hadoop and data warehousing technology can be more perfect for each other ...