DataBase Aggregation
Data aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing.
(From Wikipedia)
In DBMS (Database Management System), aggregation is the process of joining two or more entities to create a more meaningful new entity. The aggregate method is used when the entities do not make sense on their own. In order to produce aggregation between two entities that cannot be used for their own attributes, a relationship is constructed and the end product is created into a new entity. Any form of relationship can be used, such as SUM, AVG, AND, OR, and so on. A wide range of tools are available on the market for table aggregation.
When using numerical numbers as data, the following DBMS aggregation operations can be used:
| Operation Name | Description |
|---|---|
| Avg | The mean or average of the data values is returned by this function. |
| Sum | After adding the data values, this method returns the total value. |
| Count | The number of records returned by this field. |
| Maximum (Max) | Returns the highest value from a supplied set of data. |
| Minimum (Min) | The smallest value in a given set of data is returned by this method. |
| Standard Deviation (Std dev) | A statistical measure of data's dispersion or spread from the mean |
In its simplest form, data aggregation is the process of compiling typically [large] amounts of information from a given database and organizing it into a more consumable and comprehensive medium. Data aggregation can be applied at any scale, from pivot tables to data lakes, in order to summarize information and make conclusions based on data-rich findings. Because of the growing accessibility to information and importance of personalization metrics across the enterprise, the application of data aggregation has become extremely relevant. The use case is industry-agnostic and is often critical to the success and continuous improvement of organizational operations across the world.
(From https://www.pagerduty.com/resources/monitoring/learn/what-is-data-aggregation/)
(From https://www.pagerduty.com/resources/monitoring/learn/what-is-data-aggregation/)
Why is Data Aggregation Important?
In our technologically advanced world, data is constantly evolving, expanding, and becoming more convoluted with each actioned input and output. Data is one of the most valuable currencies of our time, but data without organization, segmentation, and understanding is essentially useless.
What makes data valuable is the extraction of insights that point to key trends, results, and give a better understanding of the information at hand. A process in which data is searched, gathered, and presented in a summarized, report-based form, data aggregation helps organizations to achieve specific business objectives or conduct process/human analysis at almost any scale.
Examples of Data Aggregation
Data aggregation has been widely used throughout society for countless years; but with advances in computing and technology like AI and Machine Learning, the scale and capacity of data aggregation has grown exponentially.. Examples of data aggregation can be as simple as collecting the amount of steps you took this week on your commute to work, and as complex as using a ride-sharing app to hail a car to your exact location within minutes. While the second option may sound simpler from an end-user perspective, the amount of data that needs to be computed and aggregated in order to make that ride accessible for you is astounding. The importance of data aggregation will continue to grow as technology becomes more and more embedded into our lifestyles, both at home and in the workplace.