What is a Data Warehouse? A Full Look

In simple terms, a data warehouse is a collection of data gathered from various places including sales software, marketing data, customer surveys, apps and outside systems. Data from these sources is regularly pulled and formatted for the purpose of business analysis or business intelligence.

A good example of a data warehouse is the standard use of customer surveys. A retailer may send out these surveys to clients in order to gain marketing feedback on user experience or overall experience. This data is then collected from customers and collected in a data warehouse where it can later be analyzed. Companies can also search for specific data using a data warehouse (such as the average age of a shopper), which is referred to as data mining.

There are many examples of data warehousing that provide a fast and effective solution to analyzing customer or internal data, but one of the most common scenarios is a high level of uncertainty when executing management decisions. For example, a company that does not have a data warehouse may be basing important decisions on a single source of data (often a spreadsheet) that might not be accurate. It's risky business to make high-stakes decisions without a large sampling of solid data.

Industries that benefit the most from this form of business intelligence include health, insurance, travel, energy, retail and education. It can be argued, however, that any industry can benefit from better data.

How Data Warehouses Work

The best way to understand how data warehouses work is to consider each stage of warehouse development.

I. Data Objectives: In this phase, the quantitative measures that define a particular business or industry are considered and implemented.

II. Data is collected and analyzed: Most companies have some form of data collection already. Typically it includes sales software, customer relationship management (CRM) systems, marketing software systems and other forms of data collection. Data stored in spreadsheet form must also be considered and collected. It's important for data warehouse engineers to understand where data is coming from and how it is being collected and analyzed.

III. Determine current business processes: How is data currently being interpreted and what are the main performance indicators? These questions are then split into fact and dimension tables in order to gain a full understanding of how a company works and what types of data are important.

IV. Data model development: A model of data is then developed using a consistent form of measurement based on previously gathered information. Any inconsistencies in outdated data must be corrected prior to creating a data model.

V. Transform data: In order to move data from old sources to a new data warehouse, it has to be gathered and then transformed. Since this part of building a data warehouse will impact current database performance, transforming data must be done at a time when databases are not in heavy use (at night or during the weekend hours).

VI. Determine how to track and store data: What will happen to old data and how long will data be collected from a single source? These questions must be considered before building a data warehouse.

VII. Implementation: Once the data warehouse structure is complete and all data has been gathered, the warehouse can be implemented. Usually this happens in phases since the building of a warehouse takes a large amount of time.

Common Data Warehouse Models

There are three common data warehousing models including:

Operational Storage: The main difference between operational storage and enterprise of data models is that data is stored in real-time. Organizations that require real-time everyday operational data collection and analysis will be well-served by an operational storage model.

Enterprise Warehouse: All data across a company or organization is held in an enterprise-level data warehouse. The main elements of an enterprise warehouse include: division organization, scalability, heightened security, and one single approach to data collection and analysis. The end goal of an enterprise-level warehouse is to ensure that all employees with access to data can easily gather and digest information in a coherent manner. This is the best option for larger organizations with many divisions, but can also be more difficult to secure.

Data Mart: Data marts exist to store information or data about one specific subject or department. Using a data mart model will not work if various departments need access to data or data must be collected and analyzed across an organization. However, a data mart is a good choice if one particular subject or department requires regular data collection and analysis.

The best model should be chosen after a careful analysis of business objectives and current data collection software. The size of an organization should also be taken into consideration.

Best Data Warehousing Practices

A data warehouse built on mistakes is an expensive experiment. In order to build a data warehouse that functions nearly flawlessly, keep these three best practices in mind.

1. Tunnel vision: While it is important to take a careful look at current business practices and goals, it's vital to include a longer-term picture. Include a company's goals and requirements in 3 years, 5 years, and even 10 years.

2. Clearly label descriptions: Data is useless if users cannot understand intelligence reports. When building data models, it's essential to include as much description as possible to ensure detailed and coherent reports.

3. Design over efficiency: Charts and graphs that come in various colors are easier for users to digest, but research has shown that most companies prefer quick data. Building a data warehouse by prioritizing design over speed is a mistake.

Data Warehouse Certification

The two most trusted names in data warehouse certification are Oracle and IBM. However, there are a few other certification courses that are comparable. Here's a breakdown of the best available programs ranked according to industry reviews.

1.Oracle: This is one of the most trusted names in data warehousing. The Oracle certification includes various learning options such as a virtual classroom, an in-house or on-campus option, and a self-study course. Each type of course is priced differently and the Oracle certification is recognized and accepted by a number of employers.

2. IBM: IBM offers a Data Science Professional certification that encompasses data warehousing concepts. This certificate program is free and requires an examination at the end of the course in order to gain the IBM certificate.

3.Cloudera: Cloudera offers various data engineering programs and certifications. Programs are offered through a virtual classroom format and vary in price.

4. Udemy: Similar to Edureka, Udemy offers an online data warehouse training program that allows you to work at your own speed. The course does provide key warehousing concepts with a handy guide, but might not provide the well-rounded training that some other courses offer. However, Udemy's course is significantly less expensive than other options.

5. Edureka: Edureka's online data warehouse training program is designed so you can work at your own pace using online videos and tutorials. The program also includes review materials to ensure that each lesson is enforced. The best thing about this program is that you can sign in and work on the certification at any time. If you have a day job and want to study at night, this is a great option.

Data warehousing can provide companies with quick and effective data analysis culled from a number of sources. Industry professionals that go the extra mile to obtain certification in data warehousing (or even further develop data mining skills) are often in high-demand since many companies prefer to keep the construction of a data warehouse in-house.

Preparing for a Data Warehouse Specialist Interview

While every company differs, these are some of the most commonly asked questions during an interview for a data warehouse specialist.

1.What is an example of a data warehouse that you have implemented? How did you begin and end the process and what was the overall outcome?

Answer: This question asks you to effectively demonstrate your knowledge of the topic and provide a real-world example of a data warehouse that you constructed. Answer the question honestly by providing as much information as possible. Do not forget to include any difficulties or hurdles that you encountered and effectively handled.

2. What is your documentation process? How do you collect data effectively?

Answer: Consider all angles of data collection and all possible forms of currently held data when answering this question. Make sure to note that some data can be inconsistent and that it's essential to scrub data when building a warehouse. Interviewers are likely looking for an exact description of the documentation process, and being as precise as possible is wise.

3. What is your background in data warehousing?

Answer: Provide any former experience building data warehouses along with any degrees or certification. If you worked on a warehousing project before obtaining certification, make sure to include these details as well. It never hurts to add as much information to a reply as possible!

4. What are some of the common issues or roadblocks when building a data warehouse?

Answer: It's often hard to discern the right data warehouse model and can be hard to determine where all current data is kept. These are two of the most common obstacles when building a data warehouse. If you have experience with other issues, make sure to mention those as well!

5. Do you have any experience outside of warehousing that may add to your placement on a team?

Answer: It's not uncommon for recruiters to ask about additional experience that is not directly related to the position. If you have done volunteer work, spent time building other teams or as part of another team, or any other kind of work that you think might contribute to being a good team fit, let them know.

6. Can you explain what OLAP means? Can you name the three types of servers?

Answer: OLAP stands for On Line Analytical Processing, and it is the system that collects, manages, and processes data for analysis. The four types of servers are multidimensional, hybrid, specialized, and relational.

You may also be asked about current trends in the industry, so it's important to stay on top of training and certification as well as any industry news. Essential elements of a data warehousing candidate include the proper certification, experience, and any other work in a related or similar field. A number of companies seek data intelligence experts - the key is making sure that you have the right certification along with real-world experience.