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level.Use DW; CREATE TABLE Month_dim ( month_sk INT comment ' surrogate key ', month tinyint comment ' month ', month_name Varc Har (9) Comment ' month name ', quarter tinyint comment ' quarter ', year smallint comment ' year ' ) Comment ' Month Dimension table ' clustered by (Month_sk) into 8 buckets stored as orc tblproperties (' transactional ' = ' true ');In order to import the month dimension synchronously from the date dimension, the month is loaded into a preloa
Transport Method table (Shipmethod) is determined according to the relationship that should be in the actual business operation, so the database system structure design is suitable for the informatization of the business operation. Figure 3-6 Example of a relationship between tables in a business database Through the 3.1 section of the transaction processing and analysis of the comparison can be learned that business analysis needs of the database and business database has a lot of differen
Scenario 4 Data Warehouse Management DWParallel 4 100%-> must obtain a specified 4 degree of parallelism, if the number of processes obtained is less than the number of degrees of parallelism set, the operation failsParallel_min_percent: If set to 100, as aboveILM: Information Lifecycle ManagementHigh compression of dormant data on low-cost channels (e.g. tape dr
Data backupDifferential Storage Method:Version fallbackVersion conflictSchematic diagram:Workaround:Three options:1) Rational allocation of project development modulesWangcai: Articles, mails, membersXiaoqiang: Static, cache, foreground2) Reasonable allocation of project development timeWangcai: Morning developmentXiaoqiang: PM Development3) Many people develop a file at the same time, resulting in problems, then you can use the following ways to solv
Transaction fact tables, periodic snapshot fact tables, and cumulative snapshot fact tables, fact snapshotsIn the field of data warehousing there is a concept called transaction fact table, in which Chinese is generally translated into "Transaction fact tables".The Transaction fact table is one of the three basic types of fact tables in the Data warehouse modeled
Hadoop series hive (data warehouse) installation and configuration1. Install in namenodeCD/root/softTar zxvf apache-hive-0.13.1-bin.tar.gzMv apache-hive-0.13.1-bin/usr/local/hadoop/hive2. Configure environment variables (each node needs to be added)Open/etc/profile# Add the following content:Export hive_home =/usr/local/hadoop/hiveExport Path = $ hive_home/bin: $ path# Environment variables take effectSourc
On the theoretical concept of slowly changing Dimension slowly changing dimension see Data Warehouse Series-Slow slowly changing dimension (slowly changing Dimension) common three types and prototype design This article summarizes several ways to realize the slow gradual change dimension, and analyzes the logical process of changing attribute and historical attribute output. Example one: Using the slowly
Based on the Informix Data Warehouse system implementation methodology, we can divide the implementation of the data warehouse into the following steps: 1. Business Needs analysis Business requirements analysis is the basis of data war
Objective: To learn about Data Warehouses(The following is only a personal attempt to learn data warehouses.Is superficialI hope you will be able to provide guidance after reading this article.I will learn more on this basis in the future) Steps:1. Create a database data table to fill in the data2. Create a data
provided for continuous scan SQL ServerBecause the data is distributed across many physical disks, it helps provide better workload performance as a replacement for a large number of concurrent random I/O operations, and you can add the-e switch when you start SQL Server. When the-e switch is specified at startup, SQL Server can allocate 4 instead of one extents. Thus the-e switch allows SQL Server to provide up to 256 KB of I/O rates even if there a
Reprint please the head source link and the tail two-dimensional code together reprint, this article from countercurrent fish yuiop:http://blog.csdn.net/hejjunlin/article/details/52768613Preface: The data of the database is increasing every day, the overall risk of automatic deletion mechanism is too big, want to keep more historical data for query, so it is imperative to change from small hbase to large hb
Tags:order trading avoiding Themes pac bodytablecin online Database Data Warehouse - oriented Transaction oriented Theme-oriented design Storing data Store online Transaction data Storing historical da
MySQL big data warehouse receiving (single table) has been studying data warehouse receiving recently. For the INSERT command, a small amount of data can be satisfied, while for a large amount of data to be inserted into the same
Low 0.01 3000.00 Grid MED 3000.01 6000.00 Grid High 6000.01 99999999.99 Each fragment has a start value and an end value. The granularity of the segment is the gap between this paragraph and the next segment. The granularity must be the smallest possible value for the measure, and in the example of the sales order amount is 0.01. The end value of the last fragment is the maximum possible value for the sales orde
cannot be blank "), Array ("NotEmpty", "email", "email address cannot be left blank "), Array ("isEmail", "email", "Incorrect email address format "), Array ("hasOne", "email", "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 databa
I. Runtime Environment SQL> select * from v $ version;BANNER----------------------------------------------------------------Oracle Database 10g Enterprise Edition Release 10.2.0.1.0-ProdPL/SQL Release 10.2.0.1.0-ProductionCORE 10.2.0.1.0 ProductionTNS for 32-bit Windows: Version 10.2.0.1.0-ProductionNLSRTL Version 10.2.0.1.0-ProductionSQL> show parameter queryNAME TYPE VALUE-----------------------------------------------------------------------------Query_rewrite_enabled string TRUEQu
Use the java date class to generate a data warehouse dimension table Use the java date class to generate a data warehouse dimension table Date class: Returns the number of milliseconds of a relative date. Accurate to milliseconds, but does not support date internationalization and Time Zone display. The Date class evol
Generating a Data Warehouse dimension table using the Java date classDate class:The most basic date-time class that returns the number of milliseconds for a relative date. Accurate to milliseconds, but does not support the internationalization and sub-timezone display of dates. The date class began to evolve from the Java Development Package (JDK) 1.0, when it contained only a few methods of acquiring or se
Tags: scheduling filter mapping Data Warehouse Oracle proc Graphics Component RPDObieeRPD: Define the subject angle of the different analysis, determine the corresponding fact table and dimension tableReport Surface: Select the required dimensions and measures, select the desired data according to the filterVisualize: Display
Data Warehouse Architecture: Stg-ods-dw-rep/dm/other, Basic dimension date + product. Use the Python language to implement the ETL work of MySQL to Oracle, file landing method. Define HSS functions, program execution portals, define general.py public functions, and develop python.py scripts. Data architecture, each layer based on business design specificatio
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