Data Model

What is a data model?

A data model refers to a conceptual framework for thinking about data and about operations on data. A data model defines the mental toolbox of the data scientist; it has less to do with the architecture of the data systems, although architectures are often intertwined with data models.

Among the most familiar data models are those based on files and folders: data of any kind are lumped together into binary strings called files, files are collected into folders, and folders can be nested within other folders to create a folder hierarchy.

Another family of data models are various tabular models. For example, items in CSV files are listed in rows, and the attributes of each item are stored in columns. Various spreadsheet models allow forming dependencies between cells and groups of cells, including complex calculations.

The object data model is common in programming, where data are represented as objects in memory with properties and methods for transformations of such data.

Relational data model

The relational model is a way of thinking about data as sets and operations on sets. Formalized almost a half-century ago (Codd, 1969), the relational data model provides the most rigorous approach to structured data storage and the most precise approach to data querying. The model is defined by the principles of data representation, domain constraints, uniqueness constraints, referential constraints, and declarative queries as summarized below.

Core principles of the relational data model

Data representation.
Data are represented and manipulated in the form of relations. A relation is a set (i.e. an unordered collection) of entities of values for each of the respective named attributes of the relation. Base relations represent stored data while derived relations are formed from base relations through query expressions. A collection of base relations with their attributes, domain constraints, uniqueness constraints, and referential constraints is called a schema.
Domain constraints.
Attribute values are drawn from corresponding attribute domains, i.e. predefined sets of values. Attribute domains may not include relations, which keeps the data model flat, i.e. free of nested structures.
Uniqueness constraints.
Entities within relations are addressed by values of their attributes. To identify and relate data elements, uniqueness constraints are imposed on subsets of attributes. Such subsets are then referred to as keys. One key in a relation is designated as the primary key used for referencing its elements.
Referential constraints.
Associations among data are established by means of referential constraints with the help of foreign keys. A referential constraint on relation A referencing relation B allows only those entities in A whose foreign key attributes match the key attributes of an entity in B.
Declarative queries.
Data queries are formulated through declarative, as opposed to imperative, specifications of sought results. This means that query expressions convey the logic for the result rather than the procedure for obtaining it. Formal languages for query expressions include relational algebra, relational calculus, and SQL.

The relational model has many advantages over both hierarchical file systems and tabular models for maintaining data integrity and providing flexible access to interesting subsets of the data.

Popular implementations of the relational data model rely on the Structured Query Language (SQL). SQL comprises distinct sublanguages for schema definition, data manipulation, and data queries. SQL thoroughly dominates in the space of relational databases and is often conflated with the relational data model in casual discourse. Various terminologies are used to describe related concepts from the relational data model. Similar to spreadsheets, relations are often visualized as tables with attributes corresponding to columns and entities corresponding to rows. In particular, SQL uses the terms table, column, and row.

DataJoint is a refinement of the relational data model

DataJoint is a conceptual refinement of the relational data model offering a more expressive and rigorous framework for database programming (Yatsenko et al., 2018). The DataJoint model facilitates clear conceptual modeling, efficient schema design, and precise and flexible data queries. The model has emerged over a decade of continuous development of complex data pipelines for neuroscience experiments (Yatsenko et al., 2015). DataJoint has allowed researchers with no prior knowledge of databases to collaborate effectively on common data pipelines sustaining data integrity and supporting flexible access. DataJoint is currently implemented as client libraries in MATLAB and Python. These libraries work by transpiling DataJoint queries into SQL before passing them on to conventional relational database systems that serve as the backend, in combination with bulk storage systems for storing large contiguous data objects.

DataJoint comprises:

The key refinement of DataJoint over other relational data models and their implementations is DataJoint’s support of entity normalization.