data warehouse tutorial

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

is classified as a fact, the attribute tree meets the following requirements: Each node corresponds to a data source mode attribute (simple or composite attribute ). Root corresponds to the identifier of the F object. For each node v, all subsequent attributes corresponding to V are determined by the function. 1.1.3 trim and port the attribute tree 1.1.4 define dimension 1.1.5 define measurement 1.1.6 generate fact Mo

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

Before entering php data into the database, clear the phpintval and mysql int values. For more information, see. Php saves data to mysql We plan to clean up data before warehouse receiving at the dao layer, such as varchar trim and int for intval. One day, I suddenly remembered that the value range of php intval is the

Data Warehouse applications have the ability to capture large amounts of shared information from multiple decentralized departmental systems. They can effectively transform the original data of the organization into useful knowledge information, so the knowledge information can be used for strategic decision support to improve the enterprise benefit. With the hel

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

business dimension information for integrated integration, this situation is more appropriate for generating surrogate keys to master keys. Summary The construction of the dimension table seems relatively simple, in most cases the business library will be directly, but in addition to the different levels of the dimension of Redundancy (Star model), but also need to grasp the details of the following dimensions of the construction of attention, after all, the dimension of errors will

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

Big Data Current major trends (self-understanding)file system, deployment, various streams and open source tools-------ETL Development (BI project)----Data statistical analysis------data Mining, machine learning Image from the analysisfirst, about KAKFA Kafka relatedKafka, a distributed messaging system developed by LinkedIn, is written in Scala and is widely use

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

] +) + $/", $ email )){Echo json_encode (array ('res '=>-1, 'msg' => 'your mailbox format is incorrect '); exit;}$ Add_day = date ("YmdHis ");// The following is the warehouse receiving operation$ SQL = "insert into tb_register set username = '{$ name}', ages = '{$ ages}', password = '{$ pass }', address = '{$ addr}', email = '{$ email}', add_day = '{$ add_day }'";Mysqli_query ($ conn, $ SQL );Echo json_encode (array ('res '=> 1, 'msg' => 'congratulat

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

After the Oracle database has been upgraded to 11 GB recently, some problems have occurred. I began to find some things that need to be summarized. Every time I thought: next time, when I build my own data warehouse, be sure to pay attention to these details and do the work well at the initial stage of warehouse creation. 1. redo log Design1) if it can be put sep

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

The topic of today's blog is: parameter linkage solution. Environment: Data Warehouse + OLAP (cube) + Reporting Service. Effect: multiple parameters (1, 2 ,......) Achieve interaction. First, let's talk about the principle. A data warehouse consists of fact tables and dimensions. I understand that fact tables are actua

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