6 tips for big data modeling

1. Don’t try to impose traditional modeling techniques on big data

Traditional fixed record data is stable and predictable in its growth, which makes modeling relatively easy. In contrast, the exponential growth of big data is unpredictable, as is its numerous forms and sources. When websites consider modeling big data, modeling should focus on building open and flexible data interfaces because people never know when new data sources or data forms will emerge. This is not a priority in the traditional world of fixed record data.

1. Design a system instead of a model

In the traditional data domain, relational database schemas can cover most of the relationships and links between the data that a business needs to support its information. Big data is not the case, it may not have a database, or it may use a database like NoSQL, it does not require a database schema.

Because of this, the big data model should be built on the system, not the database. The system components that the big data model should include include business information requirements, enterprise governance and security, physical storage for data, integration of all types of data, open interfaces, and the ability to handle a variety of different data types.

1. Find big data modeling tools

There are commercial data modeling tools that support Hadoop and big data reporting software like Tableau. One of the requirements is that IT decision makers should include the ability to build data models for big data when considering big data tools and methods.

1. Focus on data that is critical to the business of the business

Businesses enter large amounts of data every day, and most of this big data is irrelevant. It doesn’t make sense to create a model that contains all the data. A better approach is to identify and model big data that is critical to your business.

1. Provide high quality data

If the organization focuses on developing the correct definition of data and complete metadata to describe where the data comes from, what its purpose is, and so on, it can produce better data models and relationships for big data models. Better support for data models that support the business.

1. Find the key entry point for the data

One of the most popular big data carriers today is geography, depending on the business and industry of the enterprise, as well as the big data common keys that other users need. The more an enterprise can identify these common entry points in the data, the more it can design a data model that supports the critical information access path of the enterprise.