Data Warehousing Basics

A data warehouse is a non-volatile time repository within an organization and stored in a data. It is designed and facilitates analysis and reporting. It is a transaction copy data which is specifically structured with query and analysis. A data warehouse is a subject oriented and integrated with time variant and non-volatile data collection and supports with management decision making process. Data warehouse definition is mainly depends on data storage. It is to analyze and retrieve data, to transform, to extract, to load data and in managing the data. It is considered as essential components of a data warehousing system. Many other references used in data warehousing will be broader than the context. This is an expanded definition and involves Business Intelligence tools. All the tools are used for managing the metadata.

Data warehousing starts with the organization needs for unique, reliable, integrated reporting and consolidated data for analyzing data with various levels of aggregation.

Operational system and Data Warehouse

The operational system turns on the wheels of the organization. They include and take order and sign up for consumers and complaints on it. It always deal with the operational tasks and user the data warehouse count on the new warehouse and  compare with the other orders and take look at the new customers. It never deals at the time or any other questions. It requires any other rows and compresses into an answer. It is complicated users of data warehouse continuously and changes the questions.

Advantages of data warehouse

Data warehouse helps to high query success. They have complete control over the areas of data management systems.

  • Cleaning the data
  • Various types of index
  • Process of query
  • Multiple options
  • Data accessing
  • Security accessing

Disadvantages of data warehouse

There are many considerable disadvantages included and move the data from various disparate and sources of a data that translates with implementation of cost, time, data information, flexibility and with limited capability.The following are the disadvantages of data warehouse,

  • It transforms data from one data warehouse to another data warehouse and can be represented more than 50% of the data warehouse effort.
  • All the data lenders have control over their data and increase the security, responsibility and with privacy issues.
  • It is implemented with time management and increase the cost.
  • It adds new data sources and takes extra time
  • Flexibility will be used by the users and various types of data marts are used here.
  • Data will be static
  • No data drill capability
  • It is difficult to accommodate with changes and ranges with schema of data source and queries involved in it.
  • It cannot actively monitor or changes any data.

Data Warehousing Features

Data warehouse is a subject oriented, time-variant, integration and non-volatile collection of data. An operational data base will be changed on account of transactions of the product. All the business helps and analyze with previous feedback on product data. It will be as a consumer data, supplier and finally execution will have no data that are available in analyzing because the previous data is updated due to various transactions. Data warehouse is consolidated and generalized data with multi variance view. It also provides also Online Analytical Processing (OLAP) tools. All these tools will help effectively and interactively with various spaces. This analysis will result in data generalization and mining of data. The main function of data mining is to classify associate, prediction and cluster. It is also integrated with operations of OLAP to enable the interactive mining of knowledge with various level of abstraction. So that’s the reason for data warehouse and has become as an important platform for OLAP and data analysis.

Data warehouse – understanding

  • A data warehouse is a database which is kept against the organization’s database.
  • No frequent update is done within the warehouse.
  • It is possible to consolidate historical data and also helps the business organization to do its own work.
  • A data warehouse also helps the data to take important decisions.
  • It also helps the system with integration and with application system.
  • It mainly helps the consolidated historical data analysis.

Features of data warehouse

Subject oriented: The main thing of data warehouse is subject oriented. It also provides as information through the organization operations. The subjects are for customers, suppliers, revenue and sales. A data warehouse does not mainly focus on operations rather than focusing on modeling. It is constructed for integrating data from heterogeneous sources like relational database, files and flat etc. It enhances with effective analysis of data.

Time variant: Here the data are collected in data warehouse and identified with certain time period. In a data warehouse all the data gives information from the historical view.

Non-volatile: It means the old data will not be erased when a new data is added. A data warehouse is kept separate from the operational database and with frequent changes in operational database and are not reflected with any other data warehouse. A data warehouse does not require any transaction process, concurrency and recover because it is stored in and separated from the operational database.

Applications of data warehouse

A data warehouse helps the business executives in organizing, using and analyzing the data by using decision making. It serves as a role part of a plan-executed with feedback system used in the enterprise management. Data warehousing are widely used in the following fields.

  • Retail sectors
  • Financial services
  • Controlled manufacturing and
  • Banking services

Overview about Data WareHousing

Data warehousing selects for constructing and maintain the server data and define schema with some complex queries for warehousing. It is for all the reasons that are implemented with data warehouse separately when compared to operational database.  It involves many architectural alternatives that exist with many organizations that implement an integrated enterprise that collects all the information about the subjects. For e.g. customers, products, assets, personnel and sales by spanning the whole organization, however building is an enterprise warehouse. It requires consolidation data from many sources that might include external sources like stock market needs and with operational databases. It contains various data quality or use for representations, formats, data which is reconciled. It is long and complex process for requiring many business models and take years for success. Some organizations are ensuring for data that are departmental subsets. It is mainly focused on selected subjects. In marketing data it is departmental subsets that may include customer, sales and product. These data marts enables the faster roll out and does not require any separate enterprise and may lead to complex integration problems in completing the models that are not developed. They support for multi-dimensional models with typical OLAP with special data organization. It is not generally provided with commercial DBMSs targeted to OLTP.

Warehouse can be distributed for scalability, load balancing and with higher availability. They can be distributed among the architecture that the metadata repository is usually replicated with every fragment included in the warehouse. The whole warehouse is administered centrally. An alternative one will be implemented for expediency when it comes too expensive in constructing the single logically integration enterprise. Warehouse is a federation or data marts in its own repository and decentralized administration. Rolling out and designing is a complex process that consists of the following activities,

  • Explaining about architecture
  • Capacity planning
  • Storage servers
  • OLAP server and tools
  • Client tools
  • Warehouse schema
  • Data placement
  • Accessing methods
  • Data extraction
  • Load
  • Source used by gateways
  • Transformation
  • Cleaning
  • View definition
  • Scripts
  • Other metadata
  • Implementing end user applications
  • Warehouse applications

These systems have variety data extraction and cleaning tools in refreshing and loading the utilities for warehouse population. It is usually an implementation gateways with standard interface like ODBC, SQL, Oracle, Informix and Sybase connect and Enterprise gateway. Data cleaning is done since the data warehousing are used in decision making. It is more important in correcting them. It involves large volumes of data from various sources. There is high probability of errors. It helps in  detecting the data with high payoff. It becomes necessary in field lengths, assignments, missing entries and violation with constants.


Data warehousing tools

Data warehousing is an online analytical processing which have essential features of decision support. It has been increasing in the database industry. The main focus is on commercial products and services which are available with basic database management and offers too many areas. Decision support requires with different needs on database technology compared with traditional transaction applications. It provides data overview with OLAP technologies with an emphasis with new needs and requirements. We explain back end tools for cleaning, extracting and loading data into a data warehouse. It is a multi-dimensional data model with OLAP typical with front end client tools for both data analysis and query. It is a metadata management for managing the warehouse. It helps in surveying the state of art. This paper identifies many research issues that are related to database community that worked for many years. It is just the beginning that is addressed.

This overview is mainly based on tutorial with author presentation at the VLDB conference. It is a collection of decision support with basic technologies that are aimed for enabling the knowledge worker to have better decisions. It has been explosive growth which involved in services and products that are offered with adoption of these technologies by industries. This includes database software, tools and hardware. These technologies are successful with many industries for manufacturing order, financial services, transportation, utilities, telecommunications and healthcare. It presents a data warehousing roadmap technologies. It focuses with special requirements of data warehouse place with management systems.

OLAP operations include drill-down, more than one hierarchy, rollup and pivot. Operational database are tuned in supporting the workloads of OLTP. It is to execute the complex of OLAP queries against the operational databases. It will result in unacceptable performance. It further supports the data that are missing from operational database. For e.g. understanding the trends and making the predictions that require historical data whereas in operational databases can store only the current data. It requires consolidation data from many sources that might include external sources like stock market needs and with operational databases. It contains various data quality or use for representations, formats, data which is reconciled. They support for multi-dimensional models with typical OLAP with special data organization. It is not generally provided with commercial DBMSs targeted to OLTP. It is for all the reasons that are implemented with data warehouse separately when compared to operational database.

It is implemented on extended and standard DBMSs and it is known as relational OLAP servers. These servers are stored with relational databases and support extensions to special classes and SQL servers. The methods will be efficiently implemented on the multi-dimensional data model and operations. All servers are stored directly in special data structures.

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