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Many novices are not very clear about how to analyze the multi-model database that supports data diversity. in order to help you solve this problem, the following editor will explain it in detail. People with this need can come and learn. I hope you can get something.

The database world has become a bit chaotic in terms of managing data diversity.

Not long ago, with the exception of a few leading database management system (DBMS) platforms, databases supported only a single data model. The revival of multi-model data management is changing that and bringing benefits to database architects.

Challenges posed by diversified data structures

Storing and processing data with different structures is a challenge, and there is no one-size-fits-all standard. Data comes in many forms, and some of the most popular data models include relationships, documents, keys, and graphics.

In the past, when dealing with a new data structure, we either imposed the data on the existing DBMS data model or purchased a new DBMS that supported the new structure.

For the longest time, we used SQL DBMS and its predecessors to address structured data requirements. It turns out that this applies to most of our data. Although not optimized, we still use these traditional databases for a small amount of unstructured data (in the application).

With the increase in semi-structured and unstructured data from new data sources (big data and real-time processing), we see an interest in dedicated non-relational DBMS options. These databases, often referred to as NoSQL databases, model data in a non-tabular structure.

However, the popularity of data models and new DBMS models brings difficulties. Using a variety of database technologies to manage different data models brings optimization, but adds complexity.

Solve the dilemma of single model

Dedicated single-model DBMS products optimize data storage and processing. However, adding other DBMS to the architecture adds complexity, including increased integration, development, maintenance, and operation. This forces enterprises to find better ways to deal with various data models.

Fortunately, both SQL and NoSQL DBMS vendors learn from each other's capabilities (including multiple data models) and want enterprises to rationalize their DBMS technology to a single data store. Data model support used to be a factor of difference between DBMS, but now it is a common factor.

What is a multi-model database

The fusion of data models in a single data management system gives birth to a new DBMS class, called multi-model database. Some leading DBMS options have supported multiple models for some time, but we haven't seen rapid deployment yet.

The deployment method may be different. I prefer an architecture where supported models are stored in native data types and structures in a single integrated database engine. This provides consistent data management across all models and allows multiple model access to data within a single interface.

Where to start using a multi-model database

At first glance, in the face of multilingual persistence, multi-model databases seem to develop rapidly, and multilingual persistence advocates storing data in a variety of data storage technologies according to their data uses. But what if different data models can be handled in the same DBMS?

If you have invested in multi-model DBMS and it meets or exceeds the functional and non-functional requirements of your data and applications, it is better to take advantage of the enterprise's existing technologies rather than introduce new ones.

With the advent of multi-model databases, we can now divide data models and DBMS into two aspects of decision-making:

First, determine the data structure and data model that best suits you.

Then, determine which DBMS options support the data model and the requirements of the application. In fact, you can expect to have both single-model and multi-model DBMS options in the technical architecture.

For special-purpose components of an application, a single model DBMS may provide the best data management. For all other components, multi-model DBMS will rationalize and simplify your technical architecture.

When it comes to data diversity, there is no data model for all situations. Whether you choose to use a single-model database or a multi-model database, there is no reason to force the data into a non-optimized data model.

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