What is Data Warehouse?

People are constantly exposed to fresh and evolving buzzwords, industry-specific terminology, and technical terms, all of which can be challenging to catch up with. Out of the many terms, have you ever questioned, What is Data Warehouse? If yes, then you are going to find your answer here.

This blog will help you understand the meaning of a Data Warehouse, and its history, along with the types, features, applications, and advantages it possesses.

Want to sharpen your knowledge and are too tired to read till the end, no worries, we have a loophole for the same, watch this video to learn everything about Data Warehousing!

It’s now time to directly jump into the bottom of the Data Warehouse and explore the core, so let’s begin!!

Data Warehouse Definition

The very first question that was asked at the starting of the blog is now getting answered:

A data warehouse is a location where businesses store critical information holdings such as client data, sales figures, employee data, and so on.

(DW) is a digital information system that links and unifies massive amounts of data from numerous sources.

A data warehouse is a central server system that permits the storage, analysis, and interpretation of data to aid in decision-making.

It is a storage area that houses structured data (database tables, Excel sheets) as well as semi-structured data (XML files, webpages) for tracking and reporting.

The data warehouse is the heart of the BI system, designed for reporting and analysis of data.

It is a fusion of elements and technologies that facilitates the strategic application of data.

So, how did the term” data warehouse” came into existence, let’s find out:

History of Data Warehouse

Listed below are major things in the transformation of the Data Warehouse:

• Dartmouth and General Mills collaborated to develop the concepts, components, and statistics in 1960.
• Nielsen and IRI launched dimensional data marts for retail sales in 1970.
• Tera Data Corporation introduced a database management system specially developed for strategic planning in 1983.
• The Business Data Warehouse was created by IBM employees Paul Murphy and Barry Devlin in the late 1980s.
• But Inmon Bill was the one who really articulated the idea. He was regarded as the father of the data warehouse. For the construction, use, and upkeep of the warehouse and the Corporate Information Factory, he had written on a variety of subjects.

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Data Warehouse Concepts and Terminologies

The following are the concepts and fundamentals of Data Warehousing:

1. ETL: It stands for extract, transform, and load. The technique of extracting data from the source file, transforming it into a suitable layout, and loading it into the data warehouse. 
2. Ingestion: Ingestion in the data warehouse is the process of collecting and importing data into the data warehouse through external sources.
3. Data Lake: A data lake is a centralized region wherein large quantities of structured, semi-structured, and unstructured data records are processed, stored, and secured.
4. Data Transformation: The process of changing, cleaning, and transforming data from one structure to another for analysis functions.
5. Data Profiling: Data profiling is the technique of analyzing the data, structure, and quality for analysis purposes. 
6. Partitioning: Partition is splitting complicated large tables and indexes into smaller chunks.
7. Data Mining: Data mining is one of the most useful techniques to extract valuable information from huge sets of data, also known as Knowledge Discovery in Database (KDD).
8. Data Quality: Data quality is the state of the data, reflected in its accuracy, completeness, reliability, relevance, and timeliness.
9. Data Cleaning: The process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. It is also known as Data cleansing or data scrubbing
10. Metadata: Metadata tells you everything about the usage, structure, definition, location, ownership, creation, values, source, and other features of the data sets in the warehouse.
11. Data Integration: Data integration is the process of merging data records from different sources into an integrated warehouse.

Also, check out our blog on the Difference Between OLAP and OLTP!

Database vs. Data Warehouse

How does a Data warehouse work?

A data warehouse converts relational data and other data sources into multidimensional concepts for analysis. Metadata is formed during this conversion to speed up concerns and searches. On top of this data layer is a semantic layer that organizes and maps complex data into familiar business language such as ‘product’ or ‘customer’ so analysts can quickly build analyses without knowing database table names. Finally, an analytics layer sits on top of the semantic layer, allowing authorized users to access, visualize, and interpret data.

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Hands-On: Data Warehousing with Microsoft Azure Databricks

Step 1: Visit https://azure.microsoft.com/en-in/get-started/azure-portal and log in to your Azure Portal account using your Azure credentials.

Step 2: Once logged in, search and click on the Azure Databricks.

Step 3: Now click on the Create button in order to create an Azure Databrick workspace.

Step 4: Now enter the (Resource group, Workspace name, and Region) details for creating an Azure Databricks workspace.

Step 5: Review the configuration once validation succeeds, proceed with clicking the Create button.

Step 6: Wait for a while until the deployment progress is completed.

Step 7: We can see deployment is completed; now click on the Go to resource.

Step 8: Now overview the essentials of the Azure Databricks Service. Click on the Launch Workspace.

Step 9: Signing into the Azure Databricks using Azure Active Directory single sign-on.

Step 10: After signing on to the Azure Databricks interface. Get started with Databricks to launch the Data Warehouse.

Step 11: In the left-hand navigation panel, select the SQL Warehouses. Click on the Create SQL Warehouse to create and manage the database system.

Step 12: Fill in the (Name, Cluster size, and Types) details for the new SQL warehouse.

Step 13: Wait for a while to start the running status of the data warehouse.

Step 14: Now we can see the SQL warehouses are in running status. Once it is complete, we can proceed with ingestion from external data sources.

Step 15: View the connection details (Server hostname, Port, and URL) of SQL Warehouses.

Step 16: At the left-hand navigation panel, click on the Catalog. In catalogs, we can see the newly created data warehouse ‘warehouse_test_intellipaat’ is in an active running state.

Step 17: We will add data from the data sources and upload our data file from local files.

Step 18: Browse and upload the files to create a table from the data file.

Step 19: Select and open the dataset.

Step 20: Hence, our dataset is uploaded. Preview the rows and columns schema.

Step 21: Preview the information schema of the dataset.

Step 22: Now run the query. Select * from the dataset, and make sure we are running in the same data warehouse

Step 23: Run the query. Select ‘Economy Label’ from the dataset; display the values of the ‘Economy Label’ columns.

Features/Characteristics of Data Warehouse

Some of the features of a Data Warehouse are listed below:

Integrated

Establishing a common unit of measurement for all related data in a data warehouse using data from different databases is the process of integrating data. You must store data within it in a simple and universally acceptable manner.

It must also be consistent in terms of nomenclature and layout. This type of application is useful for analyzing big data.

Non-volatile

The data warehouse is also non-volatile, which means that past data cannot be erased. The information is read-only and is only modified on a routine basis. It also helps with statistical data evaluation and comprehension of what and when events occurred. You don’t require any other complicated procedure.

Subject-oriented

Rather than company operations, a data warehouse typically provides information on a specific topic (such as sales inventory or supply chain).

Persistent

Prior data is not deleted when new data is added, making it persistent and non-volatile. Data from the past is kept for analogies, patterns, and predictive analysis.

Learn more about Data Warehouse through this Data Warehouse Tutorial!

Types of Data Warehouse

Data Warehouses (DWH) are classified into three types:

1. Enterprise Data Warehouse (EDW)

A centralized warehouse is an Enterprise Data Warehouse (EDW). It offers decision support services throughout the organization. It provides a unified approach to data organization and representation. It also allows you to categorize data by subject and grant access based on those classifications.

2. Operational Data Warehouse

When neither a data warehouse nor an OLTP system can meet a firm’s information requirements, an operations and maintenance data store,is required. The data warehouse in ODS is refreshed in real-time. As a result, it is widely used for routine tasks such as stashing records of employees.

3. Data Marts

A Data Mart is a subdivision of a data warehouse. It is specifically designed for a specific business segment, such as sales, funding, or both. Data can be gathered from sources directly and stored in an independent data mart.

Learn more about data mart and data warehouse from our comparison blog on data mart vs. data warehouse!

Data Warehouse Architecture

Data Warehouse Architecture: Basic

• Operational System: The operational system handles day-to-day transactions within an organization.
• Flat Files: Transactional data is stored in separate files with unique names.
• Meta Data: Information about data, such as author, build date, and file size, aids in query optimization.
• Lightly and Highly Summarized Data: Summarized data, constantly updated, speeds up query performance.
• End-User Access Tools: Tools like reporting, querying, analytical processing, and data mining allow business managers to interact with the data warehouse for strategic decision-making.

Data Warehouse Architecture: With Staging Area

• Staging Area: Data from various sources is cleansed and consolidated in a staging area before being loaded into the data warehouse. This aids in data processing and quality control.

Data Warehouse Architecture: With Staging Area and Data Marts

• Data Marts: Segments of the data warehouse that cater to specific business units or departments, like sales or payroll, enabling focused reporting and analysis.
• Customization: Data marts allow tailoring architecture for different groups within an organization.

Properties of Data Warehouse Architectures

1. Separation: Analytical and transactional processing are kept distinct.
2. Scalability: Architecture supports easy expansion to handle increasing data volume and user requirements.
3. Extensibility: The system can incorporate new technologies and operations without a full redesign.
4. Security: Access monitoring safeguards strategic data stored in the warehouse.
5. Administerability: Data warehouse management is straightforward.

Types of Data Warehouse Architectures

• Single-Tier Architecture
  • Uses virtual data warehouses as a multidimensional view of operational data.
  • Lack of separation between analytical and transactional processing can impact performance.
• Two-Tier Architecture
  • Involves source layer, data staging, data warehouse layer, and analysis.
  • Separates source data extraction, integration, and analytical functions.
  • Provides a clearer distinction between operational and analytical processes.
• Three-Tier Architecture
  • Source layer, reconciled layer, and data warehouse/data mart layer.
  • Reconciled layer standardizes data models across the enterprise.
  • Suitable for large-scale, enterprise-wide systems but introduces redundancy and some latency.

Each data warehouse architecture varies based on organizational needs, providing tailored solutions for efficient data management, processing, and decision-making.

Schemas in Data Warehouses

Data Warehouse schema is a logical description of a database. It gives logical information about a database management system. It helps to define how data is related within the data warehouse.

Here we have three types of schema used in a data warehouse:

1. Star Schema
2. SnowFlake Schema
3. Galaxy Schema

Star Schema:  

• The star schema is the easiest and most effective schema in a Data Warehouse. 
• It is also known as the Star-Join Schema. 
• Each dimension in a star schema is depicted with a one-dimension table.

SnowFlake Schema: 

• It is a process that completely normalizes all the dimension tables in a star schema.
• Data redundancy is eliminated by designing new dimension tables.
• The dimensions table in a snowflake schema is normalized.

Galaxy Schema: 

• Galaxy Schema is multidimensional and has a strong structure and aggregated design, which indicates it is suitable for complex database systems.
• The complexity of this schema makes it difficult to maintain.
• It is also called the Fact Constellation Schema.

Real-time Applications of Data Warehouse

Every organization, regardless of industry or size, requires a complete warehouse to attach differing sources for predicting, analyzing, reporting, business intelligence, and enabling strict discipline. We’ve compiled a list of the best data warehousing applications from various industries.

• Banking: Bankers can handle all of their existing funds more effectively with the right Data Warehousing solution. They can better analyze customer information, regulatory changes, and industry trends to help them make better decisions.
• Finance: The financial industry uses data warehousing in the same way that the banking industry does. The right solution assists the financing industry in analyzing customer expenses, allowing them to develop better strategies for maximizing profits on both ends.
• Insurance: In the insurance industry, data warehousing is required to maintain existing customer records and analyze them in order to identify client trends and bring more customers into the business.
• Services: Data warehousing is used in the services sector to keep track of customer information, financial records, and resources in order to analyze patterns and improve decision-making for positive outcomes.
• Education: Data warehousing is necessary for the educational sector to have a complete understanding of their faculty members’ and students’ data. It gives educational institutions access to real-time data feeds so that they can make valuable and informed decisions.
• Healthcare: Another critical application for data warehouses is in the healthcare industry. The warehouse houses all of the clinical, financial, and employee data, and analysis is done to gain useful insights for resource planning.

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Cloud Data Warehouse / Modern Data Warehouse

• Modern Data Warehouse is a cloud-based solution that collects and stores information. 
• It acts as a central repository for data that can be analyzed to enhance a business and make better, well-informed decisions.
• Integrating and managing data is critical to the cloud data warehouse.
• Modern data warehouses use cloud-based solutions to increase scalability, flexibility, and, of course, return on investment.
• To deliver more flexible data processing and analytics from multiple data sources, use a cloud data warehouse.

Data Warehouse Tools

Following are the top 5 data warehouse tools that organizations can use to streamline their data warehousing workflows.

• Microsoft Azure: Microsoft Azure is a cloud computing platform introduced by Microsoft in 2010. Microsoft Azure is a cloud computing service provider that enables users to create, examine, implement, and handle applications and services using Microsoft-managed data centers. Azure is a publicly available cloud computing platform that provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
  It provides a range of plans to meet the needs of any application, from small to massive web applications. Running virtual machines or containers in the cloud is one of Microsoft Azure’s most popular applications.
• CloverDX: CloverDX is a data integration platform created for individuals who need complete and comprehensive control over what they’re doing when attempting to solve complex problems in high-stress environments, and who would rather buy best-of-breed tools than develop their own. It communicates with other external systems.
  CloverDX provides private assistance for company-grade.
  • Host nodes or cluster nodes in the cloud or on-premises.
  • Generate expandable frames to save and share money with colleagues.
  • Process and transformation automation and orchestration
• Tableau: Many Business Intelligence industries use this tool for visualizing data. It helps to analyze complex data in a simple format. Data visualizations created with this tableau tool are in the form of dashboards and worksheets. Data that is created by the tableau tool is easily understood by anyone in the industry at any level. Even a non-technical person who does not have any knowledge about technology can understand this data.
  Features of Tableau
  • Import all sizes and ranges information.
  • It manages the metadata.
• Exadata: The Exadata cloud infrastructure hosts Oracle’s “autonomous data warehouse.” To automate administrative tasks, the self-driving platform employs adaptive machine learning. These include tuning and patching, as well as tracking, improving, and safeguarding your database. It is simple to set up an autonomous Exadata data warehouse. Begin by specifying tables and loading your data with a few mouse clicks. To improve flexibility and efficiency, the system makes use of parallelism and columnar processing.
• MariaDB: MariaDB is a high-performance database with support for customer-facing applications. It can also be used to create a columnar database for real-time analytics. Massive parallel processing (MPP) is also used in the solution. As a result, you can run SQL queries across hundreds of billions of rows.

Know the difference between Data Lake and Data Warehouse.

Advantages and Disadvantages of Data Warehouse

Advantages of Data Warehouse

• Easy Integration: When your DW is integrated successfully, it adds value to operational business applications such as CRM systems. Because of its difficulty, a data warehouse can convert information into a more simple, manageable form, allowing your team members to easily understand what’s been presented to them.
• Rapid Data Retrieval: How many times have you needed information but forgotten where you stored it? You’ll never lose track of your data once you’ve entered it into your DW. By undertaking a quick search, you can find the statistic and further analyze it without wasting time.
• Increase the Data Analytics’ Power and Speed: Impulse and instinct are the polar opposites of business intelligence and data analytics. BI and analytics require high-quality, standardized data that is timely and ready for data mining. This power and speed are enabled by a data warehouse, which offers a competitive advantage in key business sectors ranging from CRM to HR to sales success to quarterly reporting.
• Enhances the consistency and quality of data: Your business generates data in a variety of formats, including structured and unstructured data, social media data, and data from sales campaigns. A data warehouse converts this data into the constant formats required by your analytics platforms. A data warehouse also guarantees that the information generated by various business divisions is of the same quality and standard, allowing for a more efficient feed for analytics.

Disadvantages of Data Warehouse

• Maintenance Fees: One of the benefits and drawbacks of your DW is its ability to update on a regular basis. This is great for the business owner who wants the best and most up-to-date features, but these upgrades are usually not cheap.
  If you want to have the latest technology at your fingertips, you can expect to spend more than your initial investment, including regular system maintenance.
• Preparation Takes Time: While a data warehouse’s primary responsibility is to ease your business data, the majority of your work will be in entering the raw data. While the job the DW does for you is extremely helpful and comfortable, this is the majority of work you’ll have to do manually, as the DW needs to perform many other functions for you.
• Unnoticed flaws in the source system: Hidden issues associated with the source networks that supply the data warehouse may be discovered after years of non-discovery. Some fields, for example, may accept nulls when entering new property information, resulting in staff entering imperfect property data, even if it was available and relevant.

Data Warehouse in Data Engineering and Data Science

Data Engineering:

• In data engineering, the point of interest is in designing, building, and maintaining the infrastructure critical for reporting, storage, and processing. Data warehouses are a key aspect of this infrastructure, providing a structure for storing and organizing statistics.
• Data engineers are responsible for constructing and maintaining the statistical pipelines that extract, transform, and load data.

Data Science:

• Data warehouses offer a historical file of facts, permitting statistics scientists to carry out style analysis, forecasting, and different time-collection analyses.
• Data scientists additionally leverage statistics warehouses in Data Science to create fact marts or specialized subsets of facts optimized for specific analytical purposes.

Future of Data Warehouse

A data warehouse must deal with issues such as data integration, data views, quality of data, improvement, competitive strategies, and so on.

Fortunately, data warehouse automation can completely turn this scenario on its head. A data warehouse uses next-generation automation technology that depends on sophisticated design patterns and processes to automate the strategy, designing, and integration steps of the entire lifecycle.

It offers an efficient alternative to traditional data warehousing design by reducing time-consuming tasks such as ETL code generation and deployment to a database server.

Conclusion

Data warehouses are centralized data repositories that can be used to encourage business reporting and analysis. Many businesses actually use numerous data warehouses to support multiple geographies or functions within the company. Again, a data warehouse makes data integration in an organization more workable by providing a central repository of data for reporting and analysis.