Top 4 Characteristics of Data Warehouse Every Data Engineer Should Be Aware Of

As organizations develop into more significant institutions and corporations, they keep on isolating themselves both topographically and socially from the business sectors and clients they deal with. Let us take Disney, for example. It is an American company but also has a significant presence and proper operations in Asia, Europe and Australasia. There are over thousands of such examples from different fields.

These organisations produce a tremendous amount of information that was earlier kept as a by-product. But with the rise of more and more tools available, they have started focussing on changing and managing the data in simpler forms for both operational and scientific purposes. To handle and store this much data, we need a data warehouse.

We can define a data warehouse as a vault for information that can be fetched from various sources. Front end applications are used as attachments to make sense out of this enormous data. From retailers to banks, every organisation understands the importance of collecting and utilising data.

Following is a list of important data warehouse characteristics that one should be aware of:

  1. Subject-oriented
  2. Time-variant
  3. Non-volatile
  4. Integrated

1. Subject-Oriented

A data warehouse is designed in such a way that it does not need to emphasise the daily happenings. The primary task that a data warehouse is given is mostly around the modelling of data and then analysing it for different decision making processes that might affect the day to day working of the company as well as shape the long term plans.

It is also responsible for presenting the data in a simple but efficient way so that for any specific theme, it becomes effortless for the employees to make decisions.

A data warehouse is known to present data regarding a general context rather than the organisation’s ongoing project. Hence, it is said to be subject oriented because it deals with a theme-based subject and not the current happenings. In this case, some examples of themes can be sales, marketing, distribution and many more.

Learn: The What’s What of Data Warehousing and Data Mining

2. Time-Variant

When we go on to compare a data warehouse with other data management systems, it stands out with the flexibility of the time horizon it offers. Whenever any data is collected in the data warehouse, it also stores the associated time which helps us in analysing the historical data trends as well as makes it possible to refer to a past event or point of data efficiently.

In most of the cases, the data warehouse stores information of the time horizon in the record key’s structure. We can find an explicit or implicit mention of some information on the time horizon in almost every record key. Data points associated with time can range from time, week, year and many more. An important characteristic of this time datapoint is that it cannot be changed or removed once created and associated with a key.

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3. Non-Volatile

Whenever any new data points are stored in the data warehouse, the previous data is not removed or affected in any way. This property of a data warehouse makes it non-volatile.

Every datapoint is refreshed at certain time intervals and is presented in a view-only form. Non-Volatile behaviour of a data warehouse allows it to access the historical data with ease and enables it to be time-variant. This eradicates the use of any simultaneous transaction management or any reconciliation on failed processes.

Due to this non-volatile nature, there are no editing actions like deleting, updating, etc., which are usually included in other architectures. In simpler words, within the data warehouse system, there are only two types of actions –

  1. Data access
  2. Data loading

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4. Integrated

Within a data warehouse, there are multiple sources of data which leads to a distinct set and types of databases. But a data warehouse makes sure that for measuring the data, it maintains a constant unit of measurement. On top of this, the data warehouse also keeps common terminology and the encoding of all the data stored.

Must Read: Data Warehouse Architecture

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We trust that the information in this article assisted you in understanding the characteristics of data warehouses. For more information, connect with the specialists at upGrad.

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What are the functionalities of data warehousing?

Data warehouses make it possible to generalize and consolidate data in a multidimensional view. Along with the multidimensional view, you also receive various effective tools for enhanced analysis of the data. Some of the functionalities of data warehousing are:
1. Data Extraction – It is the process of gathering data from several sources.
2. Data Cleaning – Finding as well as correcting the errors found in data.
3. Data Transformation – The process of converting the data into the warehouse format from the legacy format.
4. Data Loading – Here, the data is sorted, consolidated, summarized, and also checked for integrity.
5. Refreshing – In this process, updating takes place from the data sources to warehouses.

What are the pros and cons of data warehousing?

Data has become the most important aspect for every business and organization in the world. Proper collection and analysis of data have turned out to be a necessary task. Data warehousing can really benefit your business or organization with everything implemented right.
1. Competitive advantage – There is a massive return on investment when the decision-makers understand the demands, trends, and customers based on the available data to improve their services.
2. Enhancement of decision-makers productivity – Decision-makers can effectively analyze the data before coming to any decision based on the stored data.
3. Cost-effective – All the data is in one place. Everything becomes easy for the organizations to manage.
1. Underestimation of data loading resources – The time needed for cleaning, uploading, and retrieving data to the warehouse is high.
2. Hidden problems in source systems – Some hidden issues are often found after years when you try to supply the data warehouse.
3. Data homogenization – Loss of some data when similar data formats are dealt with from different sources.

What is the step-by-step procedure for data warehousing?

Data warehousing is considered to be a dream for business analysts because all the information about the entire organization is made available in a single place. A step-by-step procedure has to be followed to build the entire data warehouse to make this really happen.
1. Determining the business objectives
2. Collection and Analysis of information
3. Identifying the core business processes
4. Constructing a Conceptual Data Model
5. Locating different data sources and planning data transformations
6. Set tracking durations
7. Implementing the strategic plan

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