![]() Consequently, not all the links in the chain can be changed with one simple command, nor at the same time. Different departments within an organization can be viewed as links in a chain, with some using different technologies. The greater the variety of technologies being used, the more complex the organization, and its physical data model. While it remains technology agnostic, a logical model should not just be the least common denominator of data definitions with the risk of making compromises to fit the most constraining technology.” Complexity and Scale “We think that there’s a need to expand the definition of a logical model. Obviously, information architecture departments want to leverage that investment, even though the technology is evolving and becoming more complex.” “We see that companies have accumulated conceptual and logical models to describe their business and the information systems of the enterprise. Modern data contains complex nested data types, and it can be polymorphic, requiring much more effort to translate a traditional logical model into each of the very different physical schemas different technologies use.ĭesmarets commented on polyglot data models, saying: There is a need for new data models representing data that is at-rest and data that is in-motion. However, many organizations are working with traditional logical models that fall short of this goal. With a polyglot data model, data services can use and interact with different database technologies, offering multiple ways of handling and accessing data. The expression “polyglot data model” means multiple database technologies are used to read specific types of data. Now the data modelers need to understand and integrate the characteristics of each technology so the physical data model can truly leverage the respective benefits.” Polyglot Data ModelsĪ “polyglot” is someone who knows and speaks many languages. “It used to be that they were just generating DDLs, and that was fairly simple in terms of target technology. So, the environment is getting drastically more complex than in the past. Data is consumed in many more ways with machine learning, natural language processing, artificial intelligence, blockchain, and others. ![]() Each of them with a very different storage model. “They perform polyglot persistence using different types of databases with specialty databases from a variety of NoSQL vendors” said Desmarets. Nowadays enterprises have embraced modern IT architectures based on APIs and microservices, including complex communication protocols with message queuing, remote procedure calls, etc. But at the scale used by large enterprises today, these models get complex.” Those statements tended to be fairly generic, with fairly minor differences in functionality and SQL dialects between the different vendors. “Historically, physical Data Modeling has been generally focused on the design of single relational databases, with DDL statements as the expected artifact. According to Pascal Desmarets, Founder and CEO of Hackolade: Regardless of the type of database (columnar, multidimensional, or some other type of database), understanding the specifics of the DBMS is crucial to integrating the model. For example, when working with a relational database, it is necessary to understand how the columns, tables, and relationships between the columns and tables are organized. Implementing a physical data model requires a good understanding of the characteristics and performance parameters of the database system. Also, physical data models can generate DDL (or data definition language) statements, which are then sent to the database server. It is a database-specific model representing relational data objects (columns, tables, primary and foreign keys), as well as their relationships. Physical data models present an image of a data design that has been implemented, or is going to be implemented, in a database management system.
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