Data Warehousing: Why it is Uber Popular in the Industry in 2022 and Beyond

Cloud computing has changed the way we do business. The ability to store, retrieve and analyze data has revolutionized modern business practices. Data has become the new oil for determining success, and businesses that control the most data have a significant edge over the competition. Enter data warehouses. What is a data warehouse, you ask? Data warehouses are an essential tool used for data management. Several global businesses have started using data warehousing to organize their data. This data can be easily retrieved from the warehouses and analyzed to derive insights that help companies make informed decisions that help them remain competitive in the market. 

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What is a Data Warehouse

Companies today generate a large amount of data in organizational systems. A data warehouse, also known as an enterprise data warehouse, is a repository that stores data from various organizational systems. When all of a business’s data is stored in one place, it becomes easier to analyze and report. 

Various teams add data to the warehouse periodically. Moreover, this ensures that the warehouse is up to date with all historical data from the business so that it can be analyzed accurately to provide valuable insights for future endeavors.

Advantages and Disadvantages of Data Warehouses

Considering how popular data warehousing has become in the industry, it is clear that there are significant advantages to adopting this technology. However, as with any other technology, it does have a few drawbacks:

Advantages

  • Rapid Data Retrieval

Data warehouses are incredibly beneficial for retrieving specific pieces of data from the enormous volume they contain. Also, a simple search can help users recover the required data for analysis. 

  • Error Inspection and Removal

Human error is extremely common. However, one needs to be extremely careful while inputting data into a data warehouse since, once entered, it cannot be edited. Thankfully, data warehouses can notice inconsistencies in the data entered and point them out to the user. Therefore, this can help avoid errors. 

  • Data Security

By adding the right tools and access controls, data warehouses provide businesses with security and privacy for their data.

Disadvantages

  • High Cost

The equipment needed to build a data warehouse is costly. Also, they can also be expensive to maintain, especially if the update schedule is frequent. 

  • Compatibility Issues

Depending on the existing computing system a business has in place, it can be an incredibly complex task to integrate a data warehouse. Moreover, it will also require highly skilled individuals to set up and manage the data warehouse.

Database vs Data Warehouse

Databases are data collections representing certain real-world elements, whereas data warehouses are repositories of historical data from multiple sources. Databases are used to record and represent data, whereas data warehouses are designed to analyze large amounts of data. Moreover, another difference between the two is the modeling techniques used to design them. ER modeling techniques are used for databases, whereas data modeling techniques are used for data warehouses.

What Can a Data Warehouse Store?

In the 1980s, when data warehouses originally became popular, they were designed to store structured data. This included data about people, products, transactions, etc. However, businesses soon wanted to store unstructured data, including documents, images, videos, emails, etc. Also, this data is harder to manipulate into easily formattable structures. 

However, modern data warehouses can store both structured and unstructured data. This gives businesses a complete picture of their operations.

What is a Data Warehouse Example?

There are numerous use cases for data warehouses. The following are specific examples of the use of data warehouses in multiple industries: 

  • Finance: In the finance sector, data warehouses are used for analyzing market trends. Moreover, they play a significant role in foreign currency exchange (forex) as well as stock trading, where small changes can lead to profound changes in outcomes. 
  • Retail: Data warehouses are used for business intelligence and forecasting in the retail sector. For example, they can track product performance, compare marketing strategies, determine pricing, etc.

data scienceHow Do Data Warehouses Work?

Data warehouse architecture is split into three tiers. These are the database server, the analytics engine, and the front end. The database server is where large amounts of data are stored. The analytics engine analyzes the data from the server. Finally, the front end represents results to clients by using reporting and data mining tools. These tiers work together to allow businesses to store, retrieve, analyze and report data.

Types of a Data Warehouse

There are primarily three types of data warehouses: 

Enterprise Data Warehouse

This centralized data warehouse helps various departments across an enterprise with decision-making. It unifies data storage and organization across the business. It allows the classification of data based on subject and lets the administration grant separate access to different departments.

Operational Data Store

This system is used when a data warehouse does not meet the reporting needs of the business. In this system, a data warehouse can be refreshed in real-time, making it best for storing employee records.

Data Mart 

Data marts are a part of data warehouses used for specific verticals like finance, accounting, sales, procurement, inventory, etc. Moreover, this kind of warehouse allows you to retrieve data directly from the source.

The Process of Creating a Data Warehouse

Creating a data warehouse involves multiple steps like collecting, processing, and organizing data from various sources. These include: 

  • Selecting the task and the analytical processes to be performed
  • Picking the adequate data sources
  • Preparing the proper software infrastructure
  • Developing the data storage architecture
  • Setting up a data exchange process
  • Processing the data
  • Managing data security and optimizing performance

Career Outlook in Data Warehouse

Data warehousing specialists configure data storage systems to keep data accessible and safe. They are also responsible for designing, building, and maintaining data warehouses. In addition, they help store and manage large amounts of data on these systems and help various departments retrieve and analyze the same. 

According to Recruiter.com, the job outlook for data warehousing specialists has been positive since 2019. Since then, vacancies in the industry have increased by 6.51 percent across the US. As a result, demand for Data Warehousing Specialists will continue rising, with an expected 11,670 new jobs in the US being filled by 2029.

According to Glassdoor, data warehouse specialists can draw an average annual salary of $102,146 as of November 2022. The salary range is between $87,000 to $120,000 per year in the US.

Why Choose Emeritus for Building Your Data Warehouse Skills?

Data engineering can be an extremely lucrative industry. However, if you want to specialize in data warehousing, you must develop specific skills. This blog covers what a data warehouse is. But to succeed as a data warehouse specialist, you need to upskill by taking suitable online courses. Emeritus brings you courses from world-class universities to give you access to the best professors and learning resources. So, take a data science course from Emeritus today to start your journey as a data warehousing specialist.

By Tanish Pradhan 

Write to us at content@emeritus.org

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