data warehouse modernization

> ODS Layer Mainly responsible for collecting business systems and keeping relevant business data for a certain period of time. Of course, you can also meet the user's query requirements for detail data, but also can be counted as a detail warehouse. > Data Warehouse Layer T

Tags: des blog http io os using AR strong for[Article Zhang Feast this article version: v1.1 Last modified: 2010.05.18 reproduced Please specify the original link: http://blog.zyan.cc/infobright/]Infobright is a MySQL-integrated open source data Warehouse software that can be used as a storage engine for MySQL, and select queries are no different than normal MySQL.　　First, the basic characteristics of Infob

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 memory, the asynchronous

The methods for modeling SQL Server four data warehouses are mainly grouped into the following four categories. The first class is the three-paradigm modeling of relational databases, and we usually use the three-normal modeling method to build various operational database systems. The second type is the three-paradigm Data warehouse model advocated by Inmon, w

Backup | Data 1: Data Warehouse schema Backup Including the database architecture and OLAP architecture; The database includes a dimension table, fact table, and other temporary or control class tables whose structure is generated by generating SQL scripts. Note: Its primary key, index and so on are to be generated; The OLAP schema is saved by default in the "C:\

Apache Tajo is a hadoop-based relational and distributed database warehouse system. At the beginning of its design, Tajo was designed to achieve low latency, scalability, and instant query through advanced database technologies, the database warehouse system that can be aggregated to make up for the shortcomings in real-time and relational transactions such as hadoop. Tajo also supports SQL standards, so yo

enterprise-level geographic database can meet the requirements. However, we recommend that you compress the raster data. If you cannot determine the compression method, use the default lz77 (lossless compression ). 3) data warehouse receiving ArcSDE manages images in two ways: consecutive raster datasets and raster directories. Each grid directory is independent

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

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

Microsoft's Azure Data Warehouse is a distributed system based on the MPP architecture:Control node is responsible for managing the system and accepting requests from users, Compute node is responsible for computing.Currently, Azure Data Warehouse has landed in the country. You can use the new portal page to manage it,

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

Tags: loading HBA datasets Organization development int checked Storage sub 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