Importance of Future-Proofing Data Warehousing/data-science-insights/importance-of-future-proofing-data-warehousing

Importance of Future-Proofing Data Warehousing

Importance of Future-Proofing Data Warehousing

Almost every business in the modern world has increased the use of real-time business processes with digital technologies. With the digital transformation, they also concentrate on automated decision making and emphasize competing using the numerous possibilities of analytics.

The strength of any enterprise relies on the capability to leverage data to stay ahead of the competition. There are three core indicators in the market that determine business performance— speed for data collection, data processing, and data analysis. As per the market research conducted by Technavio, it is predicted that cloud migration services would hit a market value growth of 7.1 billion USD by 2024, which makes an aggregate CAGR of 24%.

Let us dive deep into data science warehousing and the need for data professionals to implement future-proofing data warehousing in the rapidly changing digital world.

What is Data Warehousing?

Traditional data warehouses are slow, cumbersome to manage, difficult to procure, and incapable of utilizing the power or features of cloud-based platforms. This can lead organizations to become less agile and fail to implement any insight-driven solutions.

A cloud data warehouse is any database that can be stored in the form of a managed service in a cloud. You can further optimize the database in the public cloud for improved Business Intelligence and Analytics.

Cloud Data warehousing no longer needs any physical data center to work; you can grow or minimize the data warehouses dynamically. This can meet your changing requirements for business budgets and take care of ideal data storage.

In a cloud data warehouse, you store information from various data sources like CRM, IoT, Financial systems, and so on. Since you get structured and unified data from the cloud data warehouse, it can easily support multiple varieties of business intelligence and analytics applications.

Cloud Data Warehousing in Businesses

The modern data warehouses designed for the cloud can help companies when they deal with the massive rise in data volume. While the cloud can trigger speed, growth, simplicity, and business savings, they fuel the enterprise operations to leverage the data completely.

Amazon Redshift is the first-ever adopted cloud data warehouse which was initially available as an on-premise cloud solution. Later the tool evolved into a completely managed warehouse service and the major tool to earn a market share with adoption.

Another breakthrough is Google BigQuery, which is the ideal storage for massive volumes of data that have infrequently fed queries. Azure SQL database is yet another tool that is best for the midsize warehouse with a data volume of about 8 TB and many active users.

Interpretation of Data Warehousing in the cloud- the myth vs. reality

Here are some misconceptions that exist about cloud Data Science warehousing and the real scenario where data science professionals need to intervene in specific situations.

Myth 1: Cloud is only meant for unimportant data like videos, music, and pictures.

Reality: Most users, especially desktop and tablet users, spend on Internet storage to safely store these files. But, the cloud serves more purposes for businesses and organizations. Some of the examples are Database as a Service (DaaS), Software as a Service (SaaS), and Platform as a Service (PaaS). All these processes together can offer a lot more than a storage space.

Myth 2: You don't own any control over the files.

Reality: Many business owners feel scared that they might lose control over their files while moving to the cloud data warehousing technique. Now, there's no need to fear, you can move all the files to the cloud and still own them even without physical access to the specific content. Cloud data warehousing lets you move a file's location and enhance its security. It is simple and the files will remain all yours, but safeguarded by another entity.

Myth 3: Data warehouses are beyond budget expectations.

Reality: Organizations feel reluctant to go for cloud data warehousing options considering the cost they have to spend on it. While there can be situations where you need to spend additional bucks to move to cloud data warehousing, you should, however, analyze them from every financial viewpoint. You must calculate the cost to operate, the infrastructure, and the cost to maintain servers. Always note that you will pay only for the computing power your business demands.

Myth 4: Cloud data warehousing is not safe

Reality: Most security breaches occur as a result of the on-premise data centers. Here, the core reason behind these data leaks is human error. Hence companies should know to enhance the data sharing protocols. When you switch to a secured data storage version, it can get rid of data leaks. However, as data science professionals you shouldn't assume that the cloud warehouse is completely safe. So before you move to data migration to the cloud, you should check the security measures.

Though Cloud data warehousing promises to keep agility, workload management, scalability, cost-effectiveness, and elasticity, the engineering challenges exist if you don't take care of the major risk areas.

How should data science professionals react to Data Warehousing

When you operate on a lower end of the business spectrum, you may opt for any variety of cloud data warehouses that suit your requirements. However, with the increase in complexity and scale, the risks also increase, so you need to be careful. As data science professionals, you need to define architectural requirements as follows: The database structure, workload, service levels, number of users, and scale.

Finally, you need to perform quantified assessment and measurement, working from macro requirements and ending up utilizing the test results to deploy architectural evaluation. It is crucial to test the workload and the database in adherence to future needs.

The upcoming decade would require the modern cloud data warehouse to meet the demands of data variety, machine learning, and real-time data analytics.

It is important for data science professionals to:

  • Determine the business needs quantitatively
  • Take into concern all the architectural requirements
  • Analyze and interpret the performance and cost differences based on the architectural differences
  • Measure and perform the test– do not believe in the vendor claims alone

Wrap Up

When it comes to data science warehousing, the cloud offers multiple options for data storage and data processing. With advanced cloud management strategies, businesses can gain a competitive edge. To be aware of the pitfalls and fix possible challenges that cloud data warehousing might bring, data science education can be helpful. With the key data science skills, you can easily take data in a new direction, explore insights about the business customers, scale up operations, and solve the business challenges.

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