TECHNOLOGY

An Overview of Data Warehousing With Common Terminologies to Know

The term “data warehousing” is the consistent process of building and deploying a data warehouse. It is made with data integration derived from several heterogeneous sources that give support to analytical reports, ad-hoc, or structured queries along with decision making. It involves the processes of data consolidation, data integration, and data cleaning too.

Using the information from data warehousing for your company

Multiple technologies help you make better decisions with the data that you get from data warehousing.  These advanced technologies help the employees of your organization to optimize the warehouse effectively and fast. These technologies can collect data and analyze it to help you make the right decisions for your company based on the current information you get from this warehouse. The data you get from a warehouse can be deployed for the domains listed below-

  1. Strategies for production tuning- These strategies can be tuned optimally by repositioning products and managing their portfolios with sales compared yearly and quarterly.
  2. Analysis of the customer- You can get a detailed picture of your customer. The data will help you gain invaluable insights into the location of the customer, buying patterns, trends, budget cycles, and more.
  3. Analysis of the operations- It helps you with customer relationship management and making corrections to the environment. This information permits you to analyze the operations of the company better.

Heterogeneous database integration

There are two methods or approaches to integrate different databases. They are-

  1. Query driven
  2. Update driven

 

  1. The process of the query-driven approach

This is the conventional approach that organizations use to integrate heterogeneous databases. It uses the approach to construct wrappers as well as integrators on the top of several different databases. These integrators are called mediators.

When the query is sent to the client, there is a metadata dictionary that translates this query into the right form that involves the sites of the individual heterogeneous databases. They are mapped and go to the processor for local questions. The results of these sites are later integrated into the answer set on a global scale.

Disadvantages of the Query driven approach

Experts from esteemed data Administration Company in the USA, RemoteDBA.com, say that the above approach requires the integration of a complex nature.

  • The filtering process is complicated, rendering this approach inefficient.
  • It can be expensive for those queries that need aggregations.

 

  1. The update driven approach

This approach is different from the traditional method, and it is followed by most modern data warehouses today. The information from several heterogeneous platforms are integrated much in advance and later stored inside the warehouse. The data is available for direct queries and its analysis. It has the following benefits-

  • It gives you better quality performance
  • The information goes through the process of copying, integrating, annotating, summarizing, and restructuring in a semantic data format store in advance
  • The processing of queries does not need an interface for processing data in sources local in nature

Data warehousing utilities & tools with the functions

Given below are the critical functions of the tools as well as the utilities of data warehousing-

  • Data Extraction- This task involves the process of collecting data from several heterogeneous sources
  • Data cleaning- This task consists of finding and rectifying data errors
  • Data Transformation- This function consists of the conversion of data from the legacy format into the warehouse format
  • Data Loading- This task requires the data to be sorted, summarized, consolidated, checked for its integrity and for consolidating partitions and indices
  • Data Refreshing- This task involves updating the data sources to the warehouse.

You should note that the processes of data cleaning and transformation are crucial for enhancing the warehouse quality and data mining results.

Common data warehousing terminologies for you to note

Metadata – This means data that is about the data. It is used for representing other information, and this is why it is called metadata. For instance, the index of a book you read is the metadata of its content. You can refer to metadata as the summarized information that leads one to the extensive data.

In the context for the data warehouse, you can define metadata to be –

  • The roadmap to the data warehouse
  • The metadata defines the objects in the warehouse.
  • The metadata also serves as a directory that helps in supporting the decision- making system when it comes to locating the contents of the data warehouse.

Metadata repository- This repository is an essential part of data warehousing. It usually contains the metadata as follows-

  • Metadata of the business- information about the ownership of the data, the nature of the business, its definition, and its changing policies
  • Metadata of operations- This covers the lineage and the currency of data. The former refers to the history of the data migration and the transformation that has been applied to it. The latter refers to the active data that has been either archived in the system or purged.
  • Data mapping from the operations environment to the data warehouse- This metadata covers the source database and its content, data extraction, and partition of data. It is cleaning, rules for transformation, data refresh, and regulations for purging.
  • The data algorithms for summary- This metadata covers the dimension algorithms, the data for aggregation, summarizing, data for granularity, and more.

Data Cube

The data cube assists you to represent the data in several dimensions. This cube is defined with facts as well as dimensions that are entities depicting how the enterprise keeps data records. For instance, if an organization wishes to keep track of sales records, it can do so with data warehousing taking into account branch, time, location, and item.

Therefore, data warehousing is integral to the development and growth of a business. You should, with the guidance and consultation with trained IT managers and database administrators, get the best data warehousing solutions. They should be customized to your business’s needs so that you get all the information you need on a single platform to make better-informed decisions for your company with success!