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3 Tips on Creating Effective Big Data Models for Businesses

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Enterprises are increasingly driven by data. Database administrators, developers, and analysts need to manage, explore, and visualize it for various platforms. The data flows and relationships are defined and structured in data models. They help analyze the requirements essential for supporting the business processes and enforce business rules, regulatory compliances, and government policies on the information. The design of logical relationships becomes the basis of a physical model which consists of storage devices, databases, and files. Data modeling tools are indispensable to the process. They simplify and speed up the creation of database designs, help with creating data description language and generating reports, and much more.  

Traditional data models are created using relational database technology. However, big data is less predictable, includes both structured and unstructured data, and does not run on relational databases. The following three tips should help ensure the right approach to modeling big data.

1. Concentrate on data that is core to your business

Nowadays, large volumes of data are collected daily from social media, business transactions, and machine-to-machine data sources. The data has various formats, e.g., documents, audio, video, or web content, streams in at unprecedented speed, but has to be processed promptly. There’s no point creating models that include all data, since much of it is extraneous. Before any data modeling, identify the information that is essential and relevant to your enterprise and concentrate on modeling it. 

2. Design a system instead of a schema

The traditional relational database schema includes most of the relationships between data required for a business’ information support. However, big data may have no database or may use NoSQL, which doesn’t require a database schema to store various data types from multiple sources.

Instead of databases, create your big data models on systems. Their components should be business information requirements, security, data storage, and the integration, open interfaces, and ability to handle all types of data. Develop sound definitions for the data and metadata that describes its origin, purpose, etc. The more details you know about each piece of information, the more likely you are to place it correctly into the data models.

The more common entry points into your data you identify, the higher your odds of building a model that supports key information access paths for your enterprise. Make sure to develop open and elastic data interfaces, because a new data source or form may emerge any day now. Business objectives also change continually; it should be easy to update and change your models over time. It’s best to store them in a data repository which allows you to modify the data sets.

3. Use big data modeling tools

Various data modeling tools offer the creation of data structure from diagrams, reverse and forward engineering, import and export facility, documentation, and reporting features. It’s best to look for tools that can be integrated with big data platforms like MongoDB or Hadoop Hive.

For example, ER/Studio Data Architect allows modeling data from relational, NoSQL, and big data sources and offers native forward and reverse engineering support for Hadoop Hive. The tool helps define business processes and conceptual models that represent business goals. Comprehensive data modeling and metadata capabilities facilitate the documentation of the most important data elements, business data objects, and regulatory attributes so as to disclose their sources, dependencies, and interactions in logical and physical data models. 

Aqua Data Studio Entity Relationship Modeler is a database integrated development environment. It enables access and quick switching between 30+ data sources. Developers can access proprietary and open-source platforms, relational and NoSQL databases, embeddable, in-memory, distributed, and massively parallel processing databases, data warehouses, and even Microsoft Excel spreadsheets. 

To Recap

Design your big data models as systems, focusing on the core business information, and choose the right tools to organize corporate data and meet your business processes needs. Such data modeling helps to see the enterprise’s data relationships, find data duplication, and integrate different data sources into the business ecosystem to work together. Along with reporting, it empowers business people to spot correlations, trends, and patterns for making better-informed decisions. Reports using data from multiple sources offer unprecedented business intelligence opportunities.