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
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
server|sql| data
Partitioned views join horizontal partitioned data from a group of members so that the data looks like it comes from the same table. SQL Server 2000 distinguishes between local partitioned views and distributed partitioned views. In the local partitioned view, all related tables and views reside on the same instance of SQL Server. In a distribute
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 Warehouse, we have seen that the
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
Compare | data
Comparison of the characteristics of nine large data warehouse schemes
China Institute of Electronic Equipment Systems Engineering Wang Jiannu Lidompo
Powerful companies such as IBM, Oracle, Sybase, CA, NCR, Informix, Microsoft, and SAS have launched their own data warehousing solutions (through acqui
Basic concepts:
1. Multi-dimensional dataset: a multi-dimensional dataset is the main object in Online Analytical Processing (OLAP) and a technology that allows quick access to data in a data warehouse. A multi-dimensional dataset is a collection of data. It is usually constructed from a subset of a
server|sqlserver| data
The construction and analysis of SQL Server Data Warehouse
(i) Basic concepts:
1. Cubes: Cubes are the primary object in online analytical processing (OLAP) and are a technology that allows fast access to data in a data
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
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
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
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
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
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
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
Instance construction process and analysis
1Now we will analyze and discuss it with a simple example.Ms SQL ServerData warehouse construction process. In fact, the construction of the Data Warehouse is quite complex. It combines the front-end technology of the data warehouse
IBM DB2 Data Warehouse Edition is a set of products that combine the strengths of DB2 Data servers and the robust business intelligence infrastructure from IBM. DB2 DWE integrates Core Components for warehouse management, data conversion,
(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
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