In this data-centric world, it comes as no surprise that sooner than later, each of us is going be generating 1.7 MB of data per second. But where would all this data go? Shouldn’t there be a storage unit to safely keep all this information, so that it can be revived when needed?

What if we tell you there is such a storage unit? Unsurprisingly, it is called a Data Warehouse. It is an analytical tool containing data and information from operational sources, constructed to help with decision-making and reporting.

Today, the global data warehousing market has risen to an extent where it is expected to grow at a 16% CAGR in the following years.

So, let’s dive deep into learning about the data warehouse and its architecture.

Learn more: The What’s What of Data Warehousing and Data Mining

What is a Data Warehouse?

A place for safekeeping of all past and commutative data coming from one or more sources is called a data warehouse. The primary purpose of having a data warehouse is to smoothen the business intelligence and reporting processes of a business. It essentially performs querying and analysis on the data it stores.

Since a data warehouse has transactional data from multiple sources, it helps businesses to:

• Preserve old records
• Evaluate existing data and identify the loopholes in the operations

Business Analysis Framework to Design a Data Warehouse

Usually, a data analyst collects relevant data from the warehouse and analyzes it to help business improve their operations. Using the data warehouse comes in handy as it helps get access to data quickly and efficiently, thus enhancing overall productivity.

Moreover, you can get a comprehensive look at the customers and all the products. This way, you can ensure a smooth customer relationship.

But for all this to happen, the data analyst would need to first understand the business needs. And for this, they need to create a business analysis framework. 

Only after a business analysis framework has been constructed can we move on to designing a data warehouse. There are three views of this:

• Top-down view: In this view, you get to see the relevant information that is needed to design the warehouse.
• Data source view: It presents the data that is captured, stored, and managed.
• Data warehouse view: It lists the fact tables and dimension tables and data in the warehouse.
• Business query view: In this, you get to see the data from the perspective of the end-user.

Once you’ve viewed the data from all these viewpoints, it’s time to move on to learning about the three types of data warehouse architecture. 

Three Types of Data Warehouse Architecture

Every time you plan of designing a data warehouse for a company, you can consider a road-map for building your data warehouse and also the following three tiers of architecture.

1. Single Tier: This is majorly responsible for generating a close packet set of data and reducing its overall volume. However, this type isn’t recommended for businesses having complex data and multiple streams of data.
2. Two Tier: In this type of architecture, the data sources are split and thus, making the data organization and storage process more efficient.
3. Three Tier: This type of warehouse architecture is the most preferred kind, since it gives really valuable insights from raw data, thus producing an organized flow of data.

It consists of the following three tiers:

• The bottom tier, which contains the servers of the warehouse. Here, the data is cleansed and loaded using back-end tools.
• The middle tier consists of an OLAP server. This layer provides a user with an abstracted view of the database, acting as a connection between the end-user and the database.
• The top tier has the API and tools (Query, data mining, analysis, and reporting tools) to extract data from the warehouse.

upGrad’s Exclusive Data Science Webinar for you –

ODE Thought Leadership Presentation

Components of Data Warehouse Architecture

To make the functioning of the architecture manageable, the warehouse contains an RDBMS server, surrounded by five main components. 

Here are the five main components of the data warehouse architecture.

Data Warehouse Database

The focal part of the warehouse architecture is a databank containing all business information that makes it understandable for reporting. Clearly, this implies you have to pick which sort of database you would use in order to store the data in your warehouse.

Coming up next are the four database types that you can utilize: 

• Relational databases are the row-based databases that you generally come across or use every day. These include Microsoft SQL Server, SAP, Oracle, and IBM DB2. 
• Analytics databases are decisively created for information stockpiling to support and oversee analysis. For example, Teradata and Greenplum. 
• Data warehouse applications aren’t actually a sort of capacity databases. They are applications that offer software for data management, such as SAP Hana, Oracle Exadata, and IBM Netezza. 
• Cloud-based databases are the ones that can be facilitated and recovered on the cloud with the goal that you don’t need to acquire any hardware to set up your data warehouse. For example, Amazon Redshift, Microsoft Azure SQL, and Google BigQuery.

If you are interested to learn more about data science, check out our data science training from top universities.

Explore our Popular Data Science Certifications

Extraction, Transformation, and Loading Tools (ETL)

ETL apparatuses are fundamental to a data warehouse architecture. These help with separating information from various sources, changing it into a reasonable arrangement, and stacking it into a warehouse. 

The ETL tool you pick will decide: 

• The time consumed in information extraction
• Ways to extract data 
• Sort of changes applied and the effort needed to do as such 
• Business rule definition for information validation and cleansing to improve end-product analytics 
• Filling lost information 
• Plotting data circulation from the key safe to your BI applications

• Read our popular Data Science Articles

  Data Science Career Path: A Comprehensive Career Guide	Data Science Career Growth: The Future of Work is here	Why is Data Science Important? 8 Ways Data Science Brings Value to the Business
  Relevance of Data Science for Managers	The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have	Top 6 Reasons Why You Should Become a Data Scientist
  A Day in the Life of Data Scientist: What do they do?	Myth Busted: Data Science doesn’t need Coding	Business Intelligence vs Data Science: What are the differences?

Metadata

Metadata depicts the data warehouse and offers a system for information. It helps in developing, safeguarding, handling, and utilizing the warehouse. It is of two types: 

• Technical Metadata: It includes data that can be utilized by engineers and managers when executing warehouse development and organization tasks. 
• Business Metadata: It includes data that offers an effectively justifiable stance of the data in the warehouse.

Metadata assumes a significant role for the organizations to comprehend the data present in the warehouse and to transform it into usable information. 

Data Warehouse Access Tools 

A data warehouse uses a database or group of databases as an establishment. Corporates, for the most part, can’t work with databases legitimately. This is the reason they use several tools, including: 

• Query and reporting tools: These assist users in creating corporate reports in spreadsheets, computations, or intelligent visuals to conduct an in-depth analysis.
• OLAP devices: These help develop a multi-dimensional data warehouse and conduct analysis of big data from various perspectives. 
• Data mining tools: These systematize the methodology of recognizing clusters and connections in enormous amounts of data, utilizing statistical modeling strategies. Learn more about data mining techniques.
• Application development tools: These help make custom-fitted reports and present them in translations, expected for specific reporting purposes. 

Top Data Science Skills to Learn

Data Warehouse Bus 

It helps decide the progression of data in the warehouse. This flow can be arranged as Inflow, Upflow, Downflow, Outflow, and Meta flow. 

While designing a Data Bus, you need to think about the common measurements, facts across data marts. 

Data Marts 

This is an entrance layer utilized to get information out to the users. It is introduced as a possibility for a huge size data warehouse, as it requires only a little amount of time and money to create. In any case, there is no standard meaning of a data mart, as it varies from individual to individual. 

Simplistically, a data mart is an auxiliary of a data warehouse and is used for segmenting information, which is made for a particular user group.

Our learners also read: Top Python Courses for Free

Layers of Data Warehouse Architecture

Constructing a data warehouse is primarily dependent on a particular business. And so, each architecture has four layers. Let’s study them in detail below.

Data source layer 

The data source layer is the place where unique information, gathered from an assortment of inner and outside sources, resides in the social database. Following are the examples of the data source layer:

• Operational Data — Product information, stock information, marketing information, or HR information
• Social Media Data — Website hits, content fame, contact page completion
• Outsider Data — Demographic information, study information, statistics information

While most data warehouses manage organized data, thought ought to be given to the future utilization of unstructured data sources, for example, voice accounts, scanned pictures, and unstructured text. These floods of data are significant storehouses of information and ought to be viewed when building up your warehouse. 

Data Staging Layer 

This layer dwells between information sources and the data warehouse. In this layer, information is separated from various inside and outer data sources. Since source data comes in various organizations, the data extraction layer will use numerous technologies and devices to extricate the necessary information.

Once the extracted data has been stacked, it will be exposed to high-level quality checks. The conclusive outcome will be perfect and organized data that you will stack into your data warehouse. The staging layer contains the given parts: 

• Landing Database and Staging Area

The landing database stores the information recovered from the data source. Before the data goes to the warehouse, the staging process does stringent quality checks on it. Arranging is a basic step in architecture. Poor information will add up to inadequate data, and the result is poor business dynamic. The arranging layer is where you need to make changes in accordance with the business process to deal with unstructured information sources. 

• Data Integration Tool

Extract, Transform and Load tools (ETL) are the data tools used to extricate information from source frameworks, change, and prepare information and load it into the warehouse. 

Read: Data Scientist Salary in India

Data Storage Layer 

This layer is the place where the data that was washed down in the arranging zone is put away as a solitary central archive. Contingent upon your business and your warehouse architecture necessities, your data storage might be a data warehouse center, data mart (data warehouse somewhat recreated for particular departments), or an Operational Data Store (ODS). 

Data Presentation Layer 

This is where the users communicate with the scrubbed and sorted out data. This layer of the data architecture gives users the capacity to query the data for item or service insights, break down the data to conduct theoretical business situations, and create computerized or specially appointed reports.

You may utilize an OLAP or reporting instrument with an easy to understand Graphical User Interface (GUI) to assist users with building their queries, perform analysis, or plan their reports.

Characteristics of Data Warehouse

A data warehouse is subject-oriented, non-volatile, time-variant, and an integrated set of data to enable a quick and efficient decision-making process for an organization.

• Subject-Oriented: A data warehouse can be utilized to examine a specific branch of knowledge. For instance, “sales” can be a specific subject. 
• Integrated: An data warehouse incorporates information from different sources. For instance, source A and source B may have various methods for distinguishing an item, however, in a warehouse, there will be just a solitary method for recognizing an item. 
• Time-Variant: A warehouse contains historical data. For instance, one can recover information from 3 months, a half year, a year, or significantly older information from a data warehouse. This appears differently in relation to a transactions framework, where just the latest information is stored. For instance, a transactions framework may hold the latest location of a client, whereas a data warehouse can hold all locations related to a client. 
• Non-Volatile: One of the best characteristics of a data warehouse is that once the data is stored in it, it is impossible that it will change. Thus, recorded information in the warehouse will never be modified.

How to Use Data Warehouse Architecture?

Building up which kind of database your business or enterprise needs and how you intend to collaborate with it is crucial while looking for insights. It is likewise critical to assess who will be inspecting information and what sources they need while considering your data warehouse design.

Despite the fact that the data warehouse versus data mart banter isn’t constantly relevant for littler organizations, those with more groups, divisions, and explicit needs may profit by a data mart. The particular subject-situated nature of a data mart makes it an essential part of you’re data warehouse architecture. 

In addition, contingent upon the size of your organization, various kinds of warehouse designs might be increasingly practical. Understanding which is best relies upon your data, the size of your sets, and your business needs.

Conclusion

A data warehouse is a data science framework that contains authentic and commutative information from single or various sources. It is an excellent way to access old and new data, get insights from it, and improve business processes by analyzing the present data.

Moreover, the concepts of data warehousing are subject-oriented, as it offers data with respect to the subject rather than the association’s progressing activities. In the warehouse, incorporation implies the foundation of a typical unit of measure for every comparable datum from the various databases. As mentioned before, it is additionally non-volatile, meaning that the past information isn’t deleted when new information is entered into it. 

The time-variation characteristic of the data warehouse allows a high timeframe of realistic usability. 

There are five fundamental parts of a data warehouse. 1) Database 2) ETL Tools 3) Meta Data 4) Query Tools 5) DataMarts

The four fundamental classes of query tools are query and reporting tools, application development tools, data mining apparatuses, and OLAP tools. 

The information sourcing, change, and relocation tools are utilized for playing out all the transformations and outlines. 

In the data warehouse architecture, meta-tag assumes a significant job as it indicates the source, use, qualities, and highlights of the data in the data warehouse.

We hope that the information in this article helped you understand the basics of data warehouse architecture. For more information, get in touch with the experts at upGrad. Just drop us an email, and we will get back to you to help you with your queries.

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.