Updated April 27, 2023

What is Data Warehouse?

The Data Warehouse (DW) or the Enterprise Data Warehouse (EDW) is the essential component for Business Intelligence (BI) systems, in which the process of assembling, administering, and manipulating the data from multiple varieties of data sources is performed in order to turn up with the significant business decision-making measures, by using the EDW as a way to associate and analyze the data related to the business requirements for which the Business Intelligence is necessitated in the form of Reporting and Analysis.

Data warehouses are considered one of the most critical components of business intelligence. They serve as central repositories of integrated data obtained from multiple sources, storing current and historical data in one place. This data is used to create analytical reports for all workers throughout the enterprise. Typically, data is uploaded to the warehouse from operational systems, such as marketing or sales, passing through an operational data store and undergoing data cleansing to ensure the data’s quality before being used in the EDW for reporting. Then comes the activity of ETL (Extract, Transform, Load), which makes use of staging, data integration, and access layers to make use of key functions.

Understanding

In simple terms, a data warehouse is a system used to report and store data. The data is first generated in various systems such as RDBMS, Oracle, and Mainframes, then transferred to the data warehouse for long-term storage to be used for analytical purposes. This storage is structured to allow users from different divisions or departments within an organization to access and analyze the data according to their individual needs and requirements.

These are analytical tools that are solely built to provide support in the decision-making process and a system for reporting to users for many departments. They are also archival data, consisting of historical usage data of the organization, which is specifically not maintained in operational systems. In essence, they are used to create a single version of truth for the entire organization.

How does it Make Working so Easy?

It maintains the copy of information and data from source transaction systems.

Integrates data from multiple sources and puts it into one database or a model; therefore, a single query engine.
can be used to put data in ODS (operational data store).
Helps in mitigation of database isolation level lock problem, which was generally caused due by large, long-running, analytical queries.
Data history is maintained even if the source transactional systems are not maintaining it.
A central view across the enterprise can be seen once all the data is put from multiple resources.
Code consistency and descriptions and even fixing bad data are improved. Basically impacts the overall data quality.

Top Companies

Given below are the top companies mentioned:

Teradata: This company tops the list when it has to be about working with EDW technology. It brings about more than 30 years of history onto the table. The company has its own software Teradata which is used by most companies dealing with the data warehouse in their organizations, especially all the banks. This company always has some new innovations to bring to the table, including the latest Hadoop-based technologies.
Oracle: This is the traditional company that is the first to strike the mind when we talk about relational databases. The 12c database has been unbeatable and is known for its high-performance standards, scale, and optimized data warehousing. The compression techniques are the new features provided by this company in the EDW space.
Amazon Web services: This IaaS of Amazon in the space of cloud computing is about the whole transformation and migration of data storage and warehousing onto the cloud has given data warehousing an entirely new definition.
Cloudera: This has been among the best companies in the space of EDW and big data technology as it provides an EDH (Enterprise data hub) for a large variety of data stores that focuses on batch processing. Their EDW is based on CDH.
MarkLogic: This company provides a NoSQL database platform. This gave a new dimension as companies started to believe in the power of NoSQL after this company introduced it.

What can you do with a Data Warehouse?

Extraction
Cleansing
Transformation
Loading
Refresh
Prediction
Statistical analysis
Decision making

Working

The raw data is firstly formatted, also called cleansing and normalizing, whereby it is processed and transformed according to the business requirement and removes the inconsistencies from the raw data. It is then stored in the EDW itself. Finally, an access layer allows the applications and tools to retrieve e data in a format suitable to their needs. There is another aspect of the architecture which covers the part related to metadata which scientists and engineers mainly use to collect information about the sources, naming conventions, refresh schedules, etc.

Advantages

Given below are the advantages mentioned:

Multiple source integration
Performing new analysis
Reduced cost to access historical data
The standard single version of the truth
Helps in improving turnaround time for data analysis and reporting

Skills

Given below are the skills mentioned:

Broad vision
Communication skills
Understanding of data and processes
Ability to analyze
General systems and application knowledge

Why Should we use Data Warehousing?

We should use data warehousing to provide our organization with a single version of the truth with the required data and no other computing overhead over the processed transactional resources.
OLAP will take care of the analytical processing part, and therefore the business insights and a meaningful generation of information can also be provided with the data warehousing.

Scope

The scope of data warehousing is in any domain that has something to do with analytics and in the cloud domain these days. You can become a DW engineer or a consultant or even make your seamless way into big data technologies. You can also look forward to being a data scientist. The scope of data is endless so is the scope for data warehousing.

Why do we Need a Data Warehouse?

We need a data warehouse because it makes no sense whatsoever to use multiple source systems and not be able to fetch all the required information instantly. Also, if not accessed, the historical data doesn’t give many advantages to the organization as a whole. Therefore, generating meaningful information sets from the raw data can be done using analysis and querying tools, and therefore data warehousing comes into the picture.

Who is the Right Audience for Learning Data Warehousing Techniques?

Anyone with the right mindset, broad vision, good data crunching skills, strong querying abilities, an interest in data-related technologies, and good analytical skills is an ideal candidate to learn and begin using data warehousing technologies.

How will this Technology help in Career Growth?

This technology does the most critical part of any organization: data crunching and the ability to generate insights by analysis. Therefore, generating meaningful information from raw data can be achieved by using this technology. You can also look to transform your way into a big data ecosystem and later data science if you are familiar with the base of it.

Examples:-

Here are some examples of data warehouses:

Retail industry: In the retail industry, customer behavior can be analyzed, inventory levels can be tracked, and sales can be forecasted using a data warehouse. For example, a retailer may use a data warehouse to analyze sales trends by region, product, and time period to identify opportunities for improvement.
Healthcare industry: The healthcare industry can use a data warehouse to track patient data, medical claims, and healthcare costs. For example, a healthcare provider may use a data warehouse to analyze patient data to improve the quality of care and reduce costs.
Financial industry: The financial industry can use a data warehouse to track customer transactions, monitor market trends, and analyze risk. For example, a bank may use a data warehouse to analyze customer transaction data to identify potential fraudulent activity.
Education industry: The education industry can use a data warehouse to track student performance, monitor attendance, and analyze enrollment trends. For instance, a university can analyze student performance data using a data warehouse to identify areas that require additional support.

Conclusion

It has been the backbone of many organizations to date and will continue to be so. However, the domain and the definition are increasing with every passing day due to the emergence of so many new technologies and tools. Making your way into this space is one of the best decisions in analytics because it provides a strong foundation and helps you understand exactly how data processing works and the background processes that govern it. I hope you liked the article. Keep reading for more information.

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