Data Modelling Methods For Big Data: A Head To Head Comparison

Data and analytics have made it into the staples of the business world. These disciplines drive efficiencies, glean deeper operational insights, and contribute to higher gains. However, with the advent of Big Data and massive amounts of information, the need for effective data processing and classification is more gnawing than ever.

Why Is Data Modelling Important?

Data modeling is a vital aspect for any organization that needs to amplify its application development and unleash the true value of its data. From a technical standpoint, data modeling denotes the process of building a visual representation to demonstrate the way data will flow.

This data model then serves as a conceptual representation of data objects, relationships between data objects, and others. Data modeling helps define data as well as ensure compliance with business rules, existing requirements, and government data policies.

Simply put, it’s like cleaning and organizing your office desk so you can easily find the things you need.

The major advantages of data modeling include:

  • Less time to market

With data modeling, business users have larger control over defining business rules. It also means that the development process flows more seamlessly and with fewer amendments. Therefore, data modeling allows companies to deliver new products and services to the general user more quickly.

  • Reduced costs

When built properly, data models can minimize data redundancy and repurpose computing output. Besides, as you get rid of superfluous data, you reduce the storage and computing costs.

  • Enhanced business operations

Data modeling jolts you into vocalizing your business and its processes. It’s impossible to define your data without knowing the overriding objective of your company.

  • Efficiency

Sound data models lay the ground for enhanced user experience. Also, this technique allows teams to make the most of their data utilization.

  • Quality
    With data modeling, data statistics activities are made more uniform. It means that there are fewer chances of computing errors.

Typical Data Warehouse Modeling Techniques

Entity Relationship Modeling

An entity-relationship or ER model is a semantic data model that is built to simplify the database design process. Being a blueprint, this model can give birth to all kinds of databases, including relational and hierarchical. The ER model sits on the concepts of “entity”, “relationship” and “attribute” and aims to represent real-world objects.

ER modeling helps you avoid design errors in large databases that are extremely difficult to fix. Especially if the database is already in operation or in the testing phase. To build data warehouse models by using this schema, you must first embed and combine data in diverse systems conceptually and from the perspective of the entire organization.

The data must then be analyzed for consistency so that we can enable further decision-making processes. However, the data cannot be used directly for analysis or decision-making. The main advantage of the Entity-Relationship schema is its versatile nature.

Dimensional Modeling

Depending on your business requirements, your data model can be relational or dimensional. A relational model is a design method aimed at eliminating data redundancy. Within the dimensional model, data is denormalized to improve performance. 

Here, the data gets divided into dimensions and facts and arranged in a way that makes it easier for the user to retrieve information and create reports. The approach focuses on identifying key business processes within the business, modeling and implementing them before adding additional business processes. Thus, this model produces a schema that is compatible with high performance.

Therefore, this type of modeling allows the user to perform requirement analysis fast while keeping performance high. Prominent examples of Dimensional Modeling are the star schema and the snowflake model.

Let’s have a closer look at these two.

Star Schema

Star schema is a data warehousing architecture model which requires you to classify your model tables as either dimension or fact. When in a diagram view, this model reminds the modeler of a star with the fact table in the center and the dimension tables branching from it. 

Currently, it’s one of the basic data warehousing models and has been gaining momentum lately. The main differentiator of star schemas is that they only join the fact table with the dimension tables. It means that this type of model enables simpler and faster SQL queries.

Snowflake schema

While the star schema is an adaptation of the relational database model, the snowflake model is a version of the star schema. But unlike star models, snowflake schemas have no redundant data and have normalized dimension tables.

Overall, both schemas accelerate the speed and simplicity of reading queries and advanced data analysis. Snowflake and star models come especially useful when processing massive data sets that incorporate information from various sources.

Data Vault

Data Vault is a hybrid technique for data modeling. In most cases, a Hybrid model serves as the main Data Warehouse model and is extended with a Dimensional Model. It was created as a more functional alternative to the star scheme and the Third Normal Form (3NF) in the realm of the Enterprise or Corporate Data Warehouses. 

Data Vault can handle large sets of detailed data using smaller physical volumes compared to both 3NF and Star schema. This type of data modeling has a solid foundation based on mathematical principles that support normalized data models.

The Data Vault uses only three types of tables – Hub, Link, and Satellite – which allows modelers to keep the database design simple and elegant. Moreover, Data Vault Models are easier to build than an ER Model, while its ETL processing is configurable.

Data Vault 2.0

Data Vault 2.0 takes it a step further by including new components like Big Data, NoSQL, while boosting the performance of the existing model.

Also, the Data Vault 2.0 version brought the following updates to the table:

  • Massively parallel processing architecture to improve performance;
  • The surrogate hash key as the main way to identify objects instead of incremental IDs;
  • Direct integration with NoSQL RDBMS;
  • Support of Agile principles and popular quality management approaches such as SDLC. 

Anchor Model

The Anchor Model further normalizes the Data Vault model. It provides agile database design and immutable data storage. Furthermore, this type of modeling allows for temporal queries using regular relational databases. Among other things, Anchor modeling requires extensions only when submitting changes in a data warehouse environment as opposed to modifications.

The model manifests the traditional concepts of objects, attributes, and relationships which add a shallow learning curve. The Anchor Model also relies on the sixth normal form, which leads to a highly decomposed implementation. The latter makes up for the disadvantages of traditional database modeling. 

To sum it up, this type of modeling allows you to start small with prototyping and build your way up to an enterprise data warehouse while keeping your previous work.

The Bottom Line Each of the data modelling methods I described above improve the quality of data as well as organize your data based on specific parameters. Although this list is not excessive, I’ve hopefully shed some light on the most popular models. While choosing the right technique for your project, make your decision based on the scope, speed of data retrieval, frequency of data creation as well as access to the lowest grain of data.

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