In some situations, the data delivered from a master data system is a bit more complex than just plain data values. Therefore, Dataceen has a Query-object that can be designed to match the API of the Master Data System (There may be a need for middleware). This Query-object follows the same security rules as all other objects in Dataceen and can be used to fetch data through Dataceen.
However, this data will only be transferred through Dataceen, so no event will be saved. Also, there will for natural reasons not be possible to include such data in search engines.

The answer is both yes and no. In situations where you think that a real-time call is needed, consider using the Query-object in Dataceen explained in the question about runtime calculated data.

Typically, Dataceen is seen as real-time alongside the master data system. Additionally, when multiple master data systems are in play, Dataceen is regarded as the faster option. Here’s how that works in two different scenarios:

Using API on Master Data System:

1. The master data system writes data changes to its database (Fast).

2. The client system queries the master data system through an API (Can be slow).

Using Dataceen:

1. The master data system writes data changes to its database (Fast).

2. The master data system writes the data to Dataceen (Fast).

3. The client system queries Dataceen for the data (Fast).

Utilizing Dataceen is advantageous for several reasons:

• The minimal additional time taken to write data to Dataceen is offset by the speed gained during data queries.

• When retrieving data from multiple systems, the client can do so with a single call, as Dataceen manages the complexities on the server side.

The nature of the 3 could be described as

• Data Lake: Stores raw, unprocessed data in its native format, including structured, semi-structured, and unstructured data. Usecases are primarily Data discovery, analytics, and machine learning.

• Data Warehouse:
  Stores processed and refined data that is typically structured. Usecases would be Business intelligence, reporting, and historical data analysis.

• Data Hub: A central hub designed to integrate and manage data from different sources and distribute it to various systems. Use cases are primarily Data integration, data sharing across silos, and maintaining data consistency across systems.

Dataceen could be considered, if anyone of the 3 above, a very advanced Data Hub. While a Data Hub and Dataceen have similarities in the sense that they share data between systems, there are some big differences as well. While a Data Hub is about the data itself, Dataceen focuses on how data from different sources can work together to create better business. This is why we call it a Data Consumption platform. This, together with the built-in security, event-driven subscriptions etc, we think that Data Hub is not the best way to describe Dataceen.

First, Datamesh is an architecture with a set of really good principles, while Dataceen is a software platform. The complete answer is a bit too long so we cannot take all of it here but, in general we think that Dataceen fits extremely well in such an architecture. Here’s why:

1. The domain ownership principle in Datamesh mandates the domain teams to take responsibility for their data. 
The Dataceen concept of having a data model with scopes that serves a contract between systems and/or domain is a technical solution to achieve this.

2. The federated governance principle in Datamesh achieves interoperability of all data products through standardization, which is promoted through the whole data mesh by the governance group.
The model and scopes together with the Administration Tool in Dataceen make it possible to define security rules, data elements etc., on a federated level.

3. The idea behind the Datamesh principle of self-serve data infrastructure platform is to adopt platform thinking to data infrastructure.
Even though this is not thought of as a single software product in Datamesh, we think that the whole concept in Dataceen is a software that could serve well in such an environment.

4. The data as a product principle in Datamesh projects a product thinking philosophy onto analytical data. This principle means that there are consumers for the data beyond the domain. The domain team is responsible for satisfying the needs of other domains by providing high-quality data. 
Dataceen is a platform that handles any type of data, it is up to the implementation (or even the consumer) to decide if it is analytical or operational. However, the way Dataceen is built using event-driven technology to automatically update external databases/systems, we think that Dataceen is a technical implementation that serves this principle very well.

Before proceeding, we should discuss a key architectural principle: "every system should own its own data model." This principle implies that a master data system should be capable of altering its internal structure and data model without impacting other systems. Therefore, distinguishing between an internal and an external model is essential. If you're considering using Dataceen as a single master of data—which it excels at—it's advisable to maintain it as a separate model and then transfer the data to a shared model.