This article was published as a part of the Data Science Blogathon.
Introduction on Data Warehousing
In today’s fast-moving business environment, organizations are turning to cloud-based technologies for simple data collection, reporting, and analysis. This is where Data Warehousing comes in as a key component of business intelligence that enables businesses to improve their performance. It is important to understand what a data repository is and why it appears in global markets.
Data Warehouse Definition
A data repository can be defined as the organization’s data collection and information extracted from external sources and external data sources. Data is periodically extracted from various internal applications such as sales, marketing, and finance; customer-interface applications; and external partner programs. This data is later use to analyze and by Data scientists to make predictions.
So what is a data warehouse? First, it is a comprehensive repository of current knowledge and history designed to improve organizational performance.
Key Database Features
The main features of a data warehouse are as follows:
Subject-Oriented
The database is centered on topics as it provides smarter information on the topic rather than complete business processes. Such topics may be marketing, promotional, glossary, etc. For example, if you want to analyze your company’s sales data, you need to build a sales database focused on sales. Such a warehouse can provide important information such as ‘who was your best customer last year?’ Or ‘who could be your best customer next year?’
Integrated
Data repository is developed by combining data from different sources into a single format. Data should be stored in a secure and universally accepted repository in terms of name, format, and coding. This helps to analyze the data effectively.
Non-Volatile
The data once stored in the database must remain unchanged. All data is read-only. Previous data is not deleted when entering current data. This helps you to analyze what happened and when.
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Time-Variant
The data stored in the database is written part-time, explicitly or implicitly. An example of a time difference in the Data Warehouse is shown in the Main Key, which must have a time factor such as day, week, or month.
Database vs. Data Warehouse
Although a data warehouse and a common website share some similarities, it should not be the same concept. The big difference is that on a website, data is collected for many transaction purposes. However, in the data warehouse, data is collected on a broad scale for analysis. Databases provide real-time data, while archives store data that will be accessed through major analytical queries.
The data warehouse is an example of an OLAP system or online database response query system. OLTP is an online system for converting data, for example, an ATM. Learn more about the difference of OLTP vs. OLAP.
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Data Warehouse Architecture
Typically, the architecture of warehouse data consists of a three-phase structure.
Bottom Tier
The sub-category or data warehouse server usually represents a related website system. Background tools used to clean, convert and feed data in this layer.
Middle Tier
The middle section represents the OLAP server which can be used in two ways.
The ROLAP or Relational OLAP model is an extended website management system that displays a multi-component data process to a standard interaction process.
MOLAP or OLAP multidimensional works directly on multidimensional data and functions.
Top Tier
This is the front-end
client interface that receives data from the data store. Contains a variety of
tools such as questionnaires, analytics tools, reporting tools, and data mining
tools.
Working of Data Warehouse
Data Storage combines data and information collected from multiple sources into a single complete database. For example, a database may include customer information from an organization’s marketing programs, its email lists, website, and comment cards. It may also include confidential information about employees, salary information, etc. Businesses use those parts of the data repository to analyze customers.
Data mining is one of the features of a data warehouse that includes looking at sensible data patterns with large data volumes and creating new strategies to increase sales and profits.
Types of Data Warehouse
1. Enterprise Data Warehouse (EDW)
This type of warehouse operates as a primary or intermediate website that provides decision support services throughout the business. The advantage of this type of archive is that it provides access to the information of the various organizations, provides a cohesive approach to data representation, and allows for the processing of complex questions.
2. Operational Data Store (ODS)
This type of data warehouse updates in real time. It is often preferred for routine tasks such as keeping staff records. Required when data storage systems do not support business reporting requirements.
3. Data Mart
Data mart is a subset of data storage designed to take care of a particular department, region, or business unit. Every business department has a central database or data mart for storing. Data from the database is stored in ODS from time to time. ODS then sends the data to EDW, where it is stored and used.
Example of a Data Warehouse
Let’s look at some examples of how companies use data warehouse as an integral part of their day-to-day operations.
Investment and insurance companies use data warehouses to primarily analyze customer and market trends and integrated data patterns. In sub-sectors such as Forex and the stock market, data storage plays an important role because a single point difference can lead to significant losses across the board.
Marketing chains use data warehouses for marketing and distribution, in order to track items, evaluate pricing policies and analyze customer purchasing trends. They use warehouse models of business intelligence data and forecasting requirements.
Healthcare companies, on the other hand, use data warehouse concepts to create medical reports, share data with insurance companies and research and medical units. Healthcare systems rely heavily on business data archives because they require the latest, updated medical information to save lives.
Data Warehousing Tools
Wondering what Data warehouse tools are? However, these are software components that are used to perform several tasks on a broader set of data. These tools help to collect, read, write and transfer data from various sources. What do the data repositories support? They are designed to support tasks such as data filtering, filtering, merging, etc.
Warehouse applications can be categorized as:
- Questioning and reporting tools
- Application development tools
- Data mining tools
- OLAP tools
Other popular data storage tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply.
Benefits of Data Warehouse
Wondering why businesses need data retention? However, there are a few benefits to the data warehouse for end users.
- Improved data compatibility
- Better business decisions
- Easy access to business data for end-users
- Better data documents
- Reduced computer costs and high productivity
- Enabling end-users to ask ad-hoc queries or reports without disabling applications
- Collection of related data from various sources to the site
Companies with dedicated Data Warehouse teams come out on top in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Although data storage may be less expensive, they pay off over time.
Conclusion on Data Warehousing
I hope you have given a good answer to the question “What is a data Warehouse?” Hopefully, you should now have a good understanding of data storage areas and why they are important in modern business. Now, you have to set up a database and upload all your different sources of information to it. I have covered all the concepts that you will need to start using a Data Warehouse from architecture & working to Different Tools and hope you like it.
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Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.
