granularity in data warehouse

Brief introduction Data integration is a key concept in the Data warehouse. The design and implementation of the ETL (data extraction, transformation and loading) process is an extremely important part of the Data Warehouse solut

of other dimensional entities as 0.5. (4) classify each dimension object to identify all feasible categories. Then, the classification conditions of these types are sorted from large to small based on their granularity to obtain an ordered set of category indicators of the dimension object. (5) create a dynamic dimension for the indicator entity. Dimension entities can be divided into two types. One type refers to the dimension entities that are esse

Before you start About this series This tutorial series Informix Warehouse Feature introduces the features and features of the new client and server Software in Informix Warehouse. You can use these tools to create and deploy data Warehouse projects, to model databases on the Informix

PHP-based simple collection data warehouse receiving program [continued], php collection warehouse receiving sequel. PHP-based simple collection data warehouse receiving program [continued], php collection warehouse receiving sequ

Tags: ash t-SQL query _id important SSD round mode for copy tableWindows Azure Platform Family of articles Catalog　　In the previous article, I introduced the basic content of the MPP architectureIn this chapter, I introduce you to the architecture of Azure SQL Data Warehouse (SQL DW).　　　The 1.SQL DW is divided into head node and work node, denoted by control node and compute node　　SQL DW uses multiple work

resources at this stage. On the contrary, if you simplify it, you will regret it later. So even if the system is slow, do not simplify the process of clearing old data. 6. Avoid granularity and partition issuesThere are two major data storage problems in the data warehouse

simplify it, you will regret it later. So even if the system is slow, do not simplify the process of clearing old data. 　　6. Avoid granularity and partition issues There are two major data storage problems in the data warehouse design process. The first is how to locate an

dimension element, you must segment it by value, taking the segmented value as the actual dimension element. When determining whether an analysis metric is a dimension element or a dimension attribute, it is necessary to consider the storage space occupied by this metric and the usage frequency of the related query synthetically. It is important to emphasize that in the process of refining the content, it is necessary to solve the ambiguity problem of the index. Indicators of the same name in d

for the extract-transform-load mechanism and to purge the data for the optimal load. The safe approach is to assume that the project manager will need more than half of the project's resources at this stage. On the contrary, if you make a simplification in this area, you will certainly regret it later. So even if the system works slowly, do not simplify the process of cleaning up old data. 6. Do not avoid

PHP-based simple collection data warehouse receiving program [continued], php collection warehouse receiving sequel In the previous article, we have collected the list data on the news page. The next step is to read the URL to be collected from the database and capture the page. Create a content table However, you must

Discussion on data modeling methods in data warehouse construction The main content of this article is not to introduce some data models of the existing popular main industries, but to share some of my experiences in

Introduction:The main content of this article is not to introduce some data models of the existing popular main industries, but to share some of my experiences in data warehouse construction projects. We hope to help you summarize a set of methods that meet the current industry standards and meet the data

structure optimized for data warehouses and OLAP applications. granularity: Granularity will directly determine the level of detail that a built-in warehouse system can provide for decision support. A higher granularity indicates that the

is the temporary storage area of interface data. It prepares for the next step of data processing. Generally, the data on the ODS layer is homogeneous with that on the source system. The main purpose is to simplify subsequent data processing. In terms of data

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

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