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Original address I. Definition of metadata According to the traditional definition, metadata (Metadata) is data about data. In the Data warehouse system, metadata can help data warehouse administrators and developers of
About star schema In the construction of the Data Warehouse, the star pattern shown in the following illustration is almost the most commonly used. It is called star mode because the E-R graphic in the pattern is like a star (it feels strange to say). NBSP as shown in the figure, the center is a large fact table with some dimension tables around it. The fact table contains the primary information fo
Policy | data 6 Strategies of Data Warehouse implementation In the implementation of the data warehouse, the need for theoretical guidance, with the development of data Warehouse t
Data currently, the term Data Warehouse does not yet have a unified definition, the famous data Warehouse expert W.h.inmon in his book, "Building The Data Warehouse", gave the following
SQL ServerCategory 4Data warehouse modelingMethods are mainly divided into the following four types. The first type is the three-paradigm modeling of relational databases. We usually use the three-paradigm modeling method to build various types of operational database systems. The second category is the three-paradigm data warehouse modeling promoted by inmon,
warehouses, as follows:In general, I agree with the new generation of data warehousing, which is easy to use, efficient, extensible, data sharing, etc., but it is difficult for me to disagree with the comparison, especially in the speed, expansion two. Traditional Data Warehouse, the size of the
the search. Quick response to complex aggregate class queries: For complex analytical SQL queries such as SUM, COUNT, AVG, GROUP by Infobright the value Save design overhead. No complex Data Warehouse model design requirements (such as star model, snowflake model), no need materialized views, data parti
response to complex aggregate class queries: For complex analytical SQL queries such as SUM, COUNT, AVG, GROUP by The value of Infobright Save design overhead. No complex Data Warehouse model design requirements (such as star model, snowflake model), no need materialized views, data partitioning, index building Con
Design | Data building Data Warehouse What do you want to do? Generally, there are two main areas of data Warehouse construction: 1. Interface design with operational database. 2. The design of the data
Objective:Ready to systematize a set of distributed Data Warehouse Modeling Practice Guide, the first list of the table, is to design a goal for themselves.The first part of the basic articleChapter One concept and definition of data Warehouse1.1 Data Management System1.2 Data
The methods for modeling SQL Server four data warehouses are mainly grouped into the following four categories. The first class is the three-paradigm modeling of relational databases, and we usually use the three-normal modeling method to build various operational database systems. The second type is the three-paradigm Data warehouse model advocated by Inmon, w
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
Data Warehouse is a comprehensive technology and solution based on data management and application. The successful implementation of data warehouses has a significant impact on the cultivation of a culture of knowledge sharing. Currently, 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 lifecycle. For the flow account
This article discusses two common methods in data warehouse model design. In the application environment of data warehouses, there are two types of load: one is to answer repetitive questions, and the other is to answer interactive questions. Dynamic query has obvious interactive features. This interaction process is often called
Cloud computing and data warehousing are a reasonable couple. Cloud storage can be scaled on demand, and the cloud can contribute a large number of servers to a specific task. The common function of Data Warehouse is the local data analysis tool, which is limited by calculation and storage resources, and is limited by
Just a few days ago, a user participated in a job interview for an enterprise. He applied for the DBA of the company and was responsible for data analysis. This company successfully completed the process. Until the person in charge of the company. The owner only gave him an interview question: Let's talk about the differences between the database and the data warehouse
(Original article: ScalingtheFacebookdatawarehouseto300PB ?, This article is translated from the original article. Facebook's challenges in storage scalability in data warehouses are unique. Our Hive-based data warehouse stores more than Pb of data and is growing at a rate of TB per day. The number of
According to the Informix Data Warehouse System implementation methodology, we can divide the data warehouse implementation into the following steps: 1. Business Demand Analysis Business Requirement analysis is the basis for data warehou
Concept | Data 1. What is a data warehouse The definition of the Data warehouse is given in the book "Building the Data Warehouse" by W.h.inmon, "a
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