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Introduction to The Concept of Data Warehousing & Its Techniques

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Data warehousing

Data warehousing is a database designed to strengthen the exercise of business information: it exists to allow clients to understand and improve the implementation of business strategies. It is intended for questioning and research, rather than for exchange support, and generally contains information from past events, but may contain information from different sources. (Jain & Gosain, 2012). It helps the organization to maintain historical records and analyze data in order to gain a better understanding of the business process as well as improve the business strategies.

Difference between traditional database and data warehouse

The data warehousing is different from the traditional database system due to the following reasons such as:

  • An operational database is being developed for well-structured task and workloads, for example, looking for specific records, orders, etc. In contrast, data warehouse queries are often complicated and present a general type of information.
  • Operational data warehouse strengthens the simultaneous handle multiple transactional queries. For operational traditional databases, simultaneous control and recovery systems are required to guarantee the cordiality and consistency of the database (Tiwari, 2014).
  • The traditional database queries allow providing user privilege to only for reading and modifying operations, while data warehouse OLAP query only permits users to read privilege.

Features of data warehouse

The following the enlisted features of data warehouse such as:

  • Subject-oriented: The data warehouse is a subject-oriented database scheme which provides users with relevant information only. The subjects can be a product, suppliers, sales and revenue etc.
  • Integrated: The data warehouse is built by integrating data from the different sources which make the information and data provided by the system most reliable. The information provided by the heterogeneous system is integrated into nature.
  • Non-volatile: The data warehouses are strengthening to store a large amount of information which ensure that without deletion of previous information, new information can be added easily.
  • Time-Variant: The information stored in the data warehouse will remain ineffective with regards to time. The users can store information from the historical point of view for any particular time of their choice.

Data warehouse application areas

The data warehouse is a business-oriented technique which executive in order to organize, analyze a set of information in order to use information for effective decision making. Moreover, data warehouse serves a sole-part in the plan-execute-access system in which feedback processing plays an important for an enterprise system. some major industries in which data warehouse applications can be used such as financial services, banking services, retail sector, controlled manufacturing, and many others.

The technique of data warehousing

The data warehousing techniques are used to improve performance and normalize the complex and mixed form of data chunks into managerial pieces of information. The techniques of MySQL and Maria DB tables conversion can be used to manage the information on the data warehouse.

Fact table: The data administrator should use huge Fact table in order to retrieve multiple rows and minimum index of meaningful information. The Fact table technique uses following identifies in well structure sequences as listed below:

  • id BIGINT / INT Signed/Unsigned Not NULL/NULL Auto Increment
  • Primary key (id)
  • Secondary key or indices
  • All VARCHAR characters; which need to identify
  • ENGINE= InnoDB

The result of this query will be displayed in the summary table view. The fact tables can be used to insert or load the data warehouse as well as with the following MySQL commands such as:

INSERT INTO FACT (,) values (,) …….;

LOAD DATA;

Normalization (Dimension) table

The normalization technique is significant for data warehouse application because it effectively eliminates disk footprints as well as improve the performance of systems. The data normalization technique will help in managing the space of the data warehouse (Forcht & Cochran, 1999). The batched normalization process can be used to eliminate the subtle issue so the data warehouse. For example, queries like

INSERT IGNORE INTO <DATABASE NAME>

            SELECT DISTINCT <ENTITY NAME> From <TABLE NAME>

Common Tools of data warehousing

The following are the list of common data warehousing tools as such:

  • Amazon Redshift: The Amazon redshift is an effective and efficient data warehousing tools which are used to many organizations in order to store and analyse their enterprise system. The main benefits of the Amazon Redshift are to analysis workload performance and evaluate utilization columnar storage for the high-performance cloud-based database scheme.
  • Teradata: Teradata is licensed software which is used by data warehousing services and for feedback analysis. Teradata DWH is a relational database management system (RDBMS) which divides data on the two bases such as data analytics and marketing applications. The data and information flow works on the parallel processing component.
  • Panoply: Panoply is one the smart data warehouse management tool which is used artificial intelligence algorithms in order to manage data lifecycle, data integration, data management, and query performance optimization. Panoply store information from heterogeneous sources in just a few clicks as well as confirm for the data integrity and security in an appropriate manner.

References

Forcht, K., & Cochran, K. (1999). Using data mining and data warehousing techniques. Industrial Management & Data Systems, 99(5), 189-196. doi: 10.1108/02635579910249567

Jain, H., & Gosain, A. (2012). A comprehensive study of view maintenance approaches in data warehousing evolution. ACM SIGSOFT Software Engineering Notes, 37(5), 1. doi: 10.1145/2347696.2347705

Tiwari, V. (2014). Outer Membrane Vesicle Proteomics to Discover the Pathogenicity of Acinetobacter baumannii. Journal Of Data Mining In Genomics & Proteomics, 05(02). doi: 10.4172/2153-0602.1000e116

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