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5 Best Practices for Building a Data Warehouse

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5 Best Practices for Building a Data Warehouse

Data warehousing can be a powerful tool for creating a vault full of valuable business data, but you need to start with some best practices. A data warehouse will help businesses compile and utilize their statistics over many months and years. What should IT and business leaders be aware of before creating one?

What is Data Warehousing?

Data warehousing is the process of combining information from multiple sources in order to support analysis and business decision-making. It is used by companies to collect valuable data and turn it into actionable insight. It can be used for creating presentations such as graphs and charts. Data warehousing acts as an archive by recording and storing statistics over many months or years.

Building a data warehouse can be a big undertaking. it is important to keep in mind a few good practices when you start.

1. Cloud is King

When creating a data warehouse, businesses have to decide whether they want to use on-premises or cloud infrastructure. Cloud computing is a popular option due to its convenience, low cost, and ease of scaling.

Cloud-based data storage is the best option for most companies. On-premises data warehouses are usually only required when security is of high concern. A private cybersecurity company, for example, might benefit from a higher level of control by building a system on its own servers.

Also read: 6 Best Practices For Cloud Cost Management

2. Determine ETL vs. ELT Early

Next, IT leaders must decide what method of data integration they will employ. This choice must be made early on in the process, as it will affect the design and architecture of the warehouse.

You can choose between ETL (extract transform load) or ELT (extract load transform). Data transformation is the main difference between ETL and ELT. In an ETL model, this happens before the data is sent to the server. In an ELT model, the transformation happens after the server has loaded the data.

ETL is an older method, but it requires less processing power. This makes it ideal for servers on-premises. ETL can also be a good option if data safety is of high importance. The raw information is not sent into the warehouse so it can be cleaned up or removed if necessary beforehand. In the transformation process, for example, personal identifying information may be removed.

ELT is faster and better at dealing with unstructured data but requires more computing power. It works well with cloud warehouses. ELT is a raw data transfer, so businesses have more flexibility in how they use the information after it has been loaded.

3. Prioritize Cybersecurity

No matter what type of data warehouse an organization creates, IT leaders must always put cybersecurity first. The same applies to both cloud-based and on-premise warehouses. The majority of reputable cloud service providers today offer businesses cybersecurity features to protect their data.

Encryption can be used to secure sensitive data. Over 40% of companies encrypt sensitive information about employees and customers, according to studies.

To protect their users, businesses that handle data containing sensitive information or identifiable information must use ETL integration. It is also important to have a carefully planned identity and access strategy. This will limit who has access to the warehouse and what they can do.

4. Working Closely with Stakeholders

When creating a data warehouse, the technical aspect is crucial. But so are the stakeholders. Facilities that fail to meet the expectations of key stakeholders may be forced to restructure, delay, or even backtrack.

The warehouse developer should maintain good communication with all stakeholders during the entire project. The C-suite should be informed about the pros and cons of key decisions like ETL vs. ELT, or on-premises vs. the cloud. It is important to know the purpose of the data warehouse before making decisions.

Developers need to keep in touch with stakeholders and be prepared to adapt any requests for changes. It is also important to keep a variety of learning materials and resources available. This will help team members and stakeholders become familiar with the data warehouse system.

Even offering resources and training to protect your warehouse can help. Anti-phishing training, for example, can prevent data theft by preventing employees from accidentally disclosing sensitive information.

Also read: 6 Ways Cloud-Based Security Tools Can Help Your Business

5. Prepare to Scale

Planning for scaling can make data warehouses easier, but it is important to do so from the beginning. It is impossible to predict the future, even if an organization does not think it will have to expand its facility. The warehouse should be designed in a flexible and adaptable way.

The decision-makers must carefully consider the data that will be processed by the warehouse and its complexity. Think about both long-term and shorter-term goals. techniques such as partitioning help to break down a facility, making it modular and flexible.

Choosing a cloud-based warehouse for your data is often the right choice if there is a likelihood of upscaling down the road. Cloud storage is cheaper and easier to obtain than servers on-premises.

Getting Started in Data Warehousing

These best practices will help IT and business leaders start off right in data warehouses. This facility is a hub and repository for company data. Therefore, creating an effective warehouse that’s well-designed is crucial. These tips can help IT leaders create a flexible, functional, and secure operation, regardless of the unique goals and needs of their business.

Written by
Denis Bitson

Denis Bitson is content editor of The Next Trends. He is passionate about sharing his technical knowledge through engaging blogs and articles. Enthusiastic about exploring the latest gadgets and indulging in video games.

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