As we go about our daily lives, we make many decisions based on our past experiences. Each time we face an important decision, our brains rely on trillions of bits of information about past events. In the same way, companies generate and collect a ton of information about the past. With this information, better decisions can be made.
Companies need multiple tools to work with data because our brain serves both to process and store. A data warehouse is one of the most important tools.
We will discuss enterprise data warehouses in this article, including their types and functions, as well as how they are used for data processing. The difference between enterprise warehouses and traditional ones, the types of data warehouses that exist, and how they work will be described. The main objective is to provide information regarding the business value of each architectural and conceptual approach.
How does an Enterprise Data Warehouse work?
Knowing how much a terabyte is, you’re probably impressed with the fact that Netflix had about 44 terabytes of data back in 2016. By its size alone, it is evident why we call it a warehouse and not just a database. Let’s start with the basics.
Enterprise Data Warehouse (EDW) is a corporate repository used to store and manage all the historical business data of a company. Usually, information is gathered from different systems, including ERPs, CRMs, physical records, and other flat files. Data must be placed in a single storage facility in order to be prepared for further analysis. As a result, different business units can query it and analyze information from multiple perspectives.
Data warehouses are a powerful tool for managing huge data sets without the need to manage multiple databases. This practice provides business intelligence (BI) with a flexible, future-proof way to store data, which is a set of methods/technologies that transform raw data into actionable insights. Using the EDW as a central part of it, the system is like a human brain storing information on steroids.
What’s the difference between enterprise data warehouses and standard data warehouses?
The database of a data warehouse is always connected to raw data sources via data integration tools on the one hand and analytical interfaces on the other. Why then do we isolate the enterprise form for discussion?
Almost all warehouses offer storage that can be transformed, moved, and presented to clients. A typical data warehouse is much more limited in its structural diversity and functionality than an enterprise one. EDWs are often decomposed into smaller databases due to their complex structure and size, so end users are more comfortable querying these smaller databases. To cover the full spectrum of functionality, we’re focusing on enterprise data warehouse architecture.
A warehouse’s size, however, does not indicate the level of technical complexity, analytical and reporting capabilities, and the types of data within. To comprehend a warehouse’s core concepts and functions, let’s delve into its main elements.
Concepts and functions of enterprise data warehouses
All warehouses have the same basic concepts and functions, despite all the bells and whistles. The following pillars define a warehouse as a technological phenomenon:
The ultimate storage solution. Enterprise data warehouses serve as a centralized repository for all business data ever generated by an enterprise.
Represents the source data. EDW sources its data from the original storage spaces like Google Analytics, CRMs, and IoT devices. Dispersed data is difficult to manage. Therefore, EDW’s objective is to provide the likeness of the original source data in a single place. Despite the fact that the company always generates new, relevant data both inside and outside, managing the flow of data before it reaches the warehouse requires dedicated infrastructure.
Maintains structured data. EDW data is always structured and standardized. Users can then query this data via BI interfaces and form reports. Data warehouses are different from data lakes because of this. Data lakes hold unstructured data for analysis. In contrast to warehouses, data lakes are used mostly by data engineers/scientists to work with big data sets.
Data that is subject-oriented. Warehouses are primarily used to store business data that can be related to different domains. The data is always structured around a specific subject, called a data model, so that it can be understood. As an example of a subject, a sales region or a total sales figure can be used. Furthermore, metadata is added to explain in detail where each piece of information came from.
Dependent on time. It usually refers to historical data, since it relates to the past. The majority of stored data is usually divided into time periods to understand when and for how long a certain tendency occurred.
Totally nonvolatile. Data stored in a warehouse cannot be removed. Due to source changes, data can be manipulated, modified, or updated, but it’s never supposed to be deleted, at least not by the end user. Considering historical data, deletions are counterproductive to analysis. The data may be revised once in a few years to remove irrelevant information.
Architecture of enterprise data warehouses
Although there are many approaches that extend warehouse capabilities in one way or another, we will concentrate on the most important ones. The entire data pipeline can be divided into three layers without going into too much technical detail:
- Layer of raw data
- Ecosystems of warehouses
- The user interface
Tooling for extracting, transforming, and loading data into a warehouse is referred to as ETL. A data integration tool performs data manipulations before it is placed in a warehouse, also under the ETL umbrella. They work between the raw data layer and the warehouse.
Finding out which tool fits the needs of your data platform can be accomplished through understanding the chain of tooling in use. In the most basic form, a warehouse may take years of planning and testing to set up.
When it comes to warehousing, ETL, and BI, it’s vital that businesses consult with experts. The experts can help you with the technical aspect, but you should speak with the people who will use the data to define the business purpose.