The structure of a data warehouse consists of three layers: the bottom is the database server , where data is loaded and stored; the middle is the analytical engine that analyzes the data; and the top is the front-end client , which represents the result through analysis, reporting, and data mining tools.
A data warehouse works by collecting and organizing data into a comprehensive database. Once collected, it is sorted into various tables depending on the data type and layout .
You can even store your sensitive business details in the data warehouse, such as employee details, salary information, and others.
To add information to your warehouse, you can create tools buy bitcoin email list to pull data from third-party services like Google Analytics , or import flat files and spreadsheets.
Some warehouses will house “data marts,” which are smaller, separate collections of data from a specific business unit. Marketing agencies might create a data mart for each of their analytics clients .
Once your data is in a data warehouse, it can be used to create reports and dashboards, which illustrate performance to your clients or supervisors.
Within each database, data is organized into tables and columns. And you can define a description of the data, such as integer, data field, or sequence. Tables can be organized into schemas, which you can think of as folders.
When data is consumed, it is stored in multiple tables described by the schema. Query tools use the schema to determine which data tables to access and analyze.
This solution typically offers greater personalization and richer insights , making it perfect for diving deeper into the details of your campaigns.
Types of Data Warehouse
Enterprise Data Warehouse : Here, we have a centralized warehouse that supports decision making for different departments in an enterprise. It provides a unified approach to organizing and representing data. With it, you gain the ability to classify data according to subject matter and grant the level of access to different departments accordingly;
Operational Data Warehouse : Popularly known as ODS, it is used when an organization’s reporting needs are not met by a data warehouse or OLTP system. In ODS, a data warehouse can be updated in real-time, making it better for routine activities such as storing employee records;
Data Mart : As part of a data warehouse, Data Mart is specially designed for a specific line of business such as finance, accounts, sales, purchasing or inventory. The warehouse allows you to collect data directly from the sources.