indexing in data warehouse

Data Warehouse data volume is generally very large, we need to back up every day? This point I still do not understand, just feel that the data warehouse at the very least from the production library flow of data does not need to

Common meanspartitions, hash-join, Data Warehouse functions, materialized views, bitmap indexes, etc. are common in the Data Warehouse technology,while the tips listed below are the most commonly used optimization tools/techniques in the project, the Green background highlight part belongs to unconventional means, the

Data Warehouse is a subject-oriented (Subject oriented), integrated (integrate), relatively stable (non-volatile), data collection that reflects historical changes (time Variant). Used to support management decisions.(1) Topic-oriented: Index data in the warehouse is organiz

Kickfire: the SQL chip boosts the MySQL data warehouse to equip every MySQL database server with an SQL chip. this is a vision of Kickfire to focus on the data warehouse market. Kickfire launched the Kickfire Database Appliance test version integrated with the new SQL chip in April 14. The company said that the SQL ch

What does the granularity in the data warehouse mean? Granularity is one of the most important aspects of data warehouse design. Granularity refers to the level at which the data stored in the data unit of the

Partitioning a relational data warehouse The following sections will briefly explain the concept of a relational data warehouse, the benefits of partitioning a relational data warehouse, and the benefits of migrating to Microsoft

Model design of Data WarehouseA. Data Modeling MethodologyThe design of the Data warehouse model follows the design principle of "top-down and gradual refinement".The design of the model is divided into three stages:1, conceptual modelThe scope and use of the business, from the height of the abstract summary, that is,

In the field of data warehousing, we often hear some personal or organizational debate about the theory of data warehousing, whether it belongs to Bill Inmon or Ralph Kimball faction. Below we will describe the difference between the two. Bill Inmon Paradigm:The Data Warehouse is part of a holistic business intelligen

Data Data Warehouse Learning Experience A Concept 1. Data Warehouse: Refers to the theme-oriented, consistent, different time, stable data collection, to support the management of the decision support process. In a broad sense,

Building a data warehouse is not a simple task and should not be done by one person alone. Since data warehousing is best integrated with business practices and information systems technology, a successful data warehouse implementation requires constant coordination of both

management gradually realized: to enhance the core competitiveness of brokerage business, we must change the original "Securities trading as the center" mode of operation, the introduction of customer relationship management concept, the implementation of customer relationship management, the establishment of "customer-centric" Brokerage Business Operation mode. By understanding the customer's behavior profile, investment needs, investment trends, risk tolerance, targeted "one-on-one" personali

Recently, a friend asked, data warehouse development difficulties. Do a few years of data warehousing, talk about Data Warehouse Technical Difficulties, I personally think no, what large data query and processing,

Bill Inmon and Ralph Kimball, who were exposed to two names at school, were unfamiliar to most of the two Americans, but they were a resounding figure in the database field. Bill Inmon, known as the "Father of the Data Warehouse", he can now see a lot of scholarly papers and articles on the Web, and Wikipedia's introduction to him should be very comprehensive: in the 80 's, Inmon's "

A data warehouse needs to obtain different types of data from different data sources, and convert these huge amounts of data into available data for users, to provide data support for e

A data warehouse needs to obtain different types of data from different data sources, and convert these huge amounts of data into available data for users, to provide data support for e

Data warehouse model development methodology (for future reference) Based on the business data model, the data model of the data warehouse system is formed through the eight-step conversion process: Procedure Action

General Purpose = Take account of both OLTP and OLAPTransaction processing =OLTPData Warehouse =olapCustom DATABASE = Custom Gold dividing line (deep analysis) ******************************* Data processing can be roughly divided into two main categories: online transaction processing OLTP (on-line transaction processing), online analytical processing OLAP (On-line Analytical Processing). OLTP is the ma

1. With the production library, data changes are captured through the CDC, and data is saved to the Data warehouse through SSIS, and now it's time to build the cube SSAS for Data statistics analysis.2. After the local environment has built many dimensional datasets, how to p

First, preface　　Changes in the work content, resulting in a return to the Data Warehouse model architecture and design, so take a moment to compare the system Review Data Warehouse modeling and system building knowledge system, recorded, as a note it.Second, the model　　No matter how

This paper gives the basic concept of SQL Server Data Warehouse, and uses the example construction process to analyze, for everyone's reference! Basic concepts: 1. Cubes: Cubes are the primary object of online analytical processing (OLAP) and are a technology that allows fast access to data in a data