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Data Modeling is suddenly facing new challenges as database design not only includes traditional relational databases, but newer NoSQL databases handling large amounts of unstructured data as well. Moreover, now advanced database analysts demand the presence of Predictive Models, which are incorporated only via Artificial Intelligence or Machine Learning technologies.

Data Modeling with GunDB

This was unheard of in the database world five or six years ago. Modern databases are equipped to handle cognitive technologies and live data sources provisioned through the Cloud. The article You Still Need a Model! The Analytics Week column Trends in Data Modeling states that the biggest challenge for modern databases is handling machine data.

In the modern database world, the source data types, data structures, and data channels are all varied and complex, so the following technology trends may be most profoundly visible in Data Modeling this year:.


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The practice of using algorithms to build Data Models has been in use for some time now, but according to industry thought leaders, the use of algorithms results in automation of Data Modeling to some extent. The algorithms could be any combination of AI, ML, Natural Language Processing NLP , and statistical algorithms, but when used with other intelligent capabilities in Self-Service Data Preparation platforms, ordinary business users can develop reasonably good Data Models for data analysis.

In advanced Machine Learning, the underlying data itself helps shape the Data Models. This type of Data Modeling approach is highly useful in Predictive Modeling, when dealing with huge datasets and repetitive Deep Learning tasks through multiple layers of data.

Normalized Data Models

Predictive Modeling is best used to determine root causes of fraud, churn, or upsell. When data is stored in silos, more time and effort are spent on Data Modeling while less time is reserved for enjoying the fruits of that labor. Semantic Models are high in demand as they are believed to work across disparate data types and data structures, and database architectures. Semantic Models also include vivid descriptions, which makes them user-friendly to a broad business user base.

Conceptual model

The flexibility of Semantic Models makes them easily usable in varying use cases. When data types or structures change, the models may be recalibrated with minimal programming effort. In the new version this year, adoption rates may differ as users remain concerned about security breaches through new threats.


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As one of the solemn technology trends of was to incorporate AI in everything in a few years, database designers must do their part to uphold this solemn promise. The high performance benchmarks on Linux have already set new database records. The manual reference is recommended for common cases.

It is also good for complex many-to-many relationships or large hierarchal data sets. As said, we have a collection of environments and we need to group them.

The 10 Essential Rules of Dimensional Modeling - Kimball Group

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Better Data Modeling: Color Code Your Data Model Diagrams using #SQLDevModeler

Here's how we did it! Free Resource. Like 5. Join the DZone community and get the full member experience. Join For Free. Embedded Data Models Denormalized The first concept we'll introduce is embedded data models, where a document contains inner objects. Our Restrictions As said, we have a collection of environments and we need to group them. These are the three options we had : And below are our considerations. In Options 1 and 3, an update takes only one DB call.