ods data warehouse

ETL concepts The three ETL letters represent extract, transform, and load, namely, extraction, conversion, and loading. (1) Data Extraction: extract the data required by the target data source system from the source data source system; (2) Data Conversion: Convert the

Tags: targe LAN causes HTTP extensions three-month partial PST calculationIn short, the database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.Databases generally store online transaction data, and data warehouses typically store historical data.Database design is to avoid redundancy a

Label:In short, the database is a transaction-oriented design, the Data Warehouse is a theme-oriented design.Databases generally store online transaction data, and data warehouses typically store historical data.Database design is to avoid redundancy as far as possible, generally adopt the rule of conforming to the pat

---note---Database Warehouse:1. Theme-oriented2. Data integration (cleanup)3. Time-variant of the database4. The non-volatile nature of data5. Support decision-making systemData Warehouse using parallel structureAdopt a new query optimization strategy and index structureDatabase warehouse conceptual model (

The previous article describes the backup and restore of SVN data in a Windows environment, and this article describes data migration under Linux environments. A preparatory work 1 Installation Environment 1 CentOS 7 2 available online 2 Software Requirements 1 WinSCP 2 PuTTy All of our command operations are on top of the putty, and the following is no longer emphasized, because the SVN environment of Linu

Model design of Data Warehouse A. 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 , the conceptual modelThe scope and use of the business,

Original Address http://blog.itpub.net/23659908/viewspace-1118762/ Thank you. model design of Data Warehouse A. Data Modeling Methodology The design of Data warehouse model follows the design principle of "top-down and gradual refinement". Model design is divided into three

[Author]: KwuAutomated scripts to import hive Data Warehouse on a daily scheduleCreate shell scripts, create temporary tables, load data, and convert to a formal partition table:#!/bin/sh# upload logs to hdfsyesterday= ' date--date= ' 1 days ago ' +%y%m%d ' hive-e ' use stage;create table tracklog_tmp (DA Teday string,datetime string,ip string, Cookieid string,us

In the use of hive Data Warehouse large data query, there is a common problem is that the query is slow, can not give users a quick data analysis query. For decision-makers, how to get the data of user behavior analysis at the second level is a topic, The previous approac

1. Users should consider the business scope, responsibilities, and computer performance. 2. Determine what decisions a business user wants to make with the help of a data warehouse 3. Identify the best users who use data warehouses for high-efficiency Decision Making 4. Search for potential new users and let them know about the

uses Smalltalk to write their own desktop interface in Windows environment. It runs on the SGI Challenge 150s hardware as a large, cross-platform generator that uses two Sybase SQL Server 10 databases. The front-end is a metadata server that describes the enterprise's data and, with it, users can construct their query requests directly on the screen. It generates a C code base for database queries, has specific code for each integrated package, and s

What is the difference between OLAP and OLTP and what is data warehouse?OLAP is usually called a data warehouse. However, data warehouses are only part of OLAP, not all.OLTP has more indexes than OLAP, and has high requirements on real-time performance, with many updates and

Tags: mysql infobright brighthouseInfobright is an open source MySQL Data Warehouse solution, which is based on two development on MySQL, infobright mainly implements a storage engine, but because its own storage logic and relational database is fundamentally different, so, It cannot be directly attached to MySQL as a plug-in like InnoDB, and its logical layer is the logical layer of MySQL plus its own opti

Warehouse. From here you can see that it has several features:1. The redundancy of the dimension tables is large, mainly because the dimensions are generally small (relative to the fact table), and the redundancy of the dimension tables can save a lot of space in the fact table. 2. Fact sheets are generally very large, and if queried in an ordinary way, the time to get the results generally is not acceptable to us. So it usually has to do some specia

", "email address occupied "));/*** Overwrite the method for adding data to the database of the parent class.* Perform md5 encryption on the user password first, and then call the parent class method to write the data to the database.*/Public function create ($ data ){$ Data = array_map ("addslashes", $

Using the Javadate class Data Warehouse dimension tableDate Category:, returns the number of milliseconds for a relative date. Accurate to milliseconds. However, the internationalization and sub-timezone display of dates is not supported.The date class began to evolve from the Java Development Package (JDK) 1.0, when it included only a few ways to get or set the various parts of a date

1. Environmental preparednessOs:centos 6.4Turn off SELinux and iptablesDeployment Puppet: 1.0 Puppet 3.7 Department Install puppet Source: http://yum.puppetlabs.com/puppetlabs-release-el-6.noarch.rpmComplete Puppetmaster/agent deployment, certificate signing ...PUPPETDB is a data warehouse that can query nodes, facter, report, catalog, resources and other information through restful HTTP.2. Installing PUPPE

then call the parent class method to write the data to the database.*/Public function create ($ data ){$ Data = array_map ("addslashes", $ data); // escape punctuation marks (single or double quotation marks) in the data$ Data ["

user password first, and then call the parent class method to write the data to the database. */ Public Function create ($ data ){ $ DATA = array_map ("addslashes", $ data); // escape punctuation marks (single or double quotation marks) in the data $

methods, so that the original disk on the large amount of IO overhead and CPU compression work to achieve a better tradeoff. The compression rate is approximately 90%. In addition, Vertica's write data and read data are separated, the read data in a compressed state exists on the disk, the written data is first in mem