Things that are available in the Data Warehouse

Data Mart is a section of a data warehouse supports the creation of reports and data analysis on a section or unit, operating on a single company. In some data warehouse implementations, the data mart is a miniature data warehouse. Data marts are often used to provide information to functional segments of the organization. Common examples of Data Mart is for the Sales Department, the Ministry of supply and delivery, Finance Department, top-level management, and so on. Data marts can also be used to segment data warehouse data to reflect the business is geographically located where each region is relatively autonomous. For example, a large service organization may treat regional operating centers as individual business units, each with its own data mart that contributes to the master data warehouse.

Data Warehouse is a database designed specifically for working on the process of query, reporting, and analysis. In the save data is data the business history of an organization, where such data is not stored in detail/detail. So data can last longer. Data sources on data warehouse comes from a wide variety of formats, software, platforms, and networks. These data are the result of transactions of the company/organization every day. Because it comes from different sources different, then the data in the data warehouse must be stored in a raw format.

Data Warehouse is also one of a decision support system, i.e. by storing data from various sources, organize and analyzed by policymakers. However, the data warehouse can not give a decision directly. But he can provide information that can make users become savvier in making strategic policy.

As for the General characteristics of the data warehouse is owned:

• Integrated data from a variety of sources that comes from transactional processes (OLTP)
• The data is made consistent
• Is aggregate data/conclusions the data, not the data detail
• Data last longer
• The data stored in the proper format so that the process of query and analysis can be done quickly
• The data are read-only

Three main functions that need to be done to make the data ready to be used in a data warehouse is the extraction, transformation, and loading. The third function is found in the staging area. In this staging data, provided the place and area with several functions like data cleansing, change, convert, and preparing the data to be stored and used in the data warehouse.

Data Extraction is the process of retrieving the required data from the source data warehouse and subsequently included on the staging area for processing at a later stage. In this function, we will be in touch with many different types of sources data. Data formats, different machines, software and architecture is not the same. So before this process we do, should we need to define the data source against the requirement, we will need to further facilitate the extraction of data on this.

In fact, the process of transactional data stored in a variety of formats so we rarely meet a consistent data between existing applications. Data transformation is intended to address this problem. With this data, the transformation process we perform against the data standardization on one consistent format. Some examples of inconsistencies of data can be caused by a different data type, data length, and others.

Data loading is moving data to the data warehouse. There are two loading data that did in the data warehouse. The first is the initial load, this process is done at the time has finished design and build data warehouse. The data entered is sure will be very large and take a relatively long time. Both the Incremental load, done when the data warehouse has been operated. So it will be easier to do a data extraction, transformation, and loading of data.

ELT (Extract Loading Transformation) is a variation of ETL (Extract Transformation Loading). The difference with the ETL was on the part of both of them, where on the ETL after data in transformation and extract the data on the move to a data warehouse, whereas for the ELT, first extract the data and then the raw data on load directly on the data warehouse and will be in a transformation on the data warehouse. It plays a role in conducting the analysis of very large data, and faster compared to ETL.

OLTP (Online Transactional Processing) is a set of functions that work together to manage, collect, store, process and distribute information. OLTP (On-line Transaction Processing) is characterized by a large amount of data but transactions made quite simple such as insert, update, and delete. The main concern of the system conducted OLTP is querying fast, easy data for improved and accessible via computers connected to a network. OLTP-oriented process that is processing a transaction directly through computers connected to a network. For example cashier at a supermarket that uses the machine in the process of this transaction. OLTP has some characteristics of a user can be creating, updating, retrieving data for every record.

OLAP (Online Analytical Processing) is a process used to make requests against the data in the form of complex and large-volume data analyses. OLAP is a technology which processes data in a multidimensional structure in the database, providing quick answers to queries and complex analysis. Multidimensional data is data that can be modeled as attribute dimensions and size attributes. Examples of attribute dimension is the name of the item and the color of the item, while the sample size is the number of attributes of the goods.

With the analysis of OLAP, then users will easily be able to deduce the data are structured. For example in a shop selling various items of X, then the OLAP store owner can know which items most customers and items which are not sold/not sold. The existence of such analysis, the shop owner will easily pull out stuff quick which sold for in production even more.

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