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Perhaps many people understand that the data warehouse is built on the basis of multidimensional data model for OLAP data platform, through the previous article-the basic architecture of the data
The common dwh architecture is as simple as figures 2 and 3. Generally, for an enterprise, the data lifecycle is 5-7 years, especially for detailed data. The lower the data granularity level, the shorter the lifecycle, the higher the data granularity and the longer the lifec
the block, instead of indexing, to accelerate search. Quick response to complex aggregate queries: Suitable for complex analytical SQL queries, such as SUM, COUNT, AVG, and GROUP InfobrightValue Save design costs. No complex data warehouse model design requirements (such as star model and snowflake model), no materialized view, Data Partition, and index cr
trend of information processing technology is from a large number The transaction database extracts data and clears and converts it to a new storage format, that is, the data is Aggregation in a special format. With the development and improvement of this process, such special data that supports decision-making Storage is called
, information is meaningful. The basic task of data Warehouse is to organize information and reorganize it, and provide it to the corresponding management decision personnel in time. Therefore, from the perspective of industry, Data Warehouse construction is a project, is a process. The whole
1 Introduction Database has become an indispensable part of large software, database is playing a more and more important role in software system, and database design is becoming an important factor affecting software performance and robustness. As the complexity of the software architecture grows higher, developers have to design more tables to store the data they need. The more tables, the more complex t
insufficient data access capabilities, its access performance to a large amount of data is significantly reduced. With the maturity of C/S technology and the development of parallel databases, the development trend of information processing technology is from a large number The transaction database extracts data and clears and converts it to a new storage fo
. With the development and refinement of this process, the special data that supports decision making Storage is called a data Warehouse (Warehouse, DW). W. H. Inmon The Data Warehouse
-makingStorage is called Data Warehouse (DW ).W. H. Inmon defines data warehouse as a topic-oriented and integrated data warehouse that supports management decision-making processes..A
this process, such decision-making and special data storage is called Data Warehouse (DW ). W. H. Inmon defines a data warehouse as a topic-oriented, integrated, stable, and time-varying dat
applications. Query design constraints: For optimal query performance, all queries should place the condition directly on the filter key in the fact table. Queries that place constraints on a second table, such as a date-vector table, will include all partitions. Factors to consider at design time: Vector data warehouses are built around facts (scalars) and vectors, and are physically represented as star and snowflake architectures, with very few ful
In general, two monitored operational components in a data warehouse environment are the use of data and data stored in the Data warehouse. Monitoring data in a
a dataset Based on dimension tables and fact tables. It allows you to quickly access a data warehouse. Our Multidimensional Dataset structure is as follows:DETAILSREET)DETAILMARK)ADDRESSPROVINCE, CITY)TIMEYEAR, DAY)Now a simple data warehouse architecture has been establish
remains stable, and the implementation and management are simple, requiring minimal management. Commercial guarantee. The first open-source warehouse analysis database supported by the business is the officially recommended warehouse integration architecture of Oracle/MySQL. Use Cases of Infobright Big Data analysis a
based on a dimension table and a fact table to make fast access to the Data warehouse. Our cube structure is as follows: DETAIL (Sreet) DETAIL (MARK) Address (province,city) Time (Year,day) Cube study is created as follows: Click Next to create a success (STUDY), which is handled as follows: Then we should create a mining model
databases, data warehouses complement each other.To add, the purpose of the Data Warehouse scheme is to provide front-end query and analysis as the basis, because there is a large redundancy, so the need for storage is also large. In order to better serve the front-end application, the data
Data Warehouse scheme is to provide front-end query and analysis as the basis, because there is a large redundancy, so the need for storage is also large. In order to better serve the front-end application, the data warehouse must have the following advantages, otherwise it is a failed
first, the use of Sqoop data extraction1. Sqoop IntroductionSqoop is a tool for efficiently transferring large volumes of data between Hadoop and structured data storage, such as relational databases. It was successfully hatched in March 2012 and is now the top project of Apache. Sqoop has SQOOP1 and Sqoop2 two generations, and the final stable version of SQOOP1
time dimension and address dimension as an example. The creation process is the same. Click Next to create a time dimension (Time).AddressAndDetailCreate a snowflake model sharing dimension Click Next to createDetailDimension. After the creation is complete, it must be processed to take effect. After creating a dimension, you should create a multi-dimensional dataset. A multi-dimensional dataset is a dataset Based on dimension tables and fact tables. It all
Server component and Application Server component on the same computer or on two computers. These groups are displayed. Figure 3. DWE runtime architecture Eight of the nine software components in the current DB2 Data Warehouse Edition 9.1 version provide OLAP services in some way. DB2 Cube Views, SQL Warehousing ToolSQW) and an IBM Rational
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