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NoSQL database, also called Not Only SQL, is an approach to data management and database design that’s useful for very large sets of distributed data. NoSQL, which encompasses a wide range of technologies and architectures, seeks to solve the scalability and big data performance issues that relational databases weren’t designed to address. NoSQL is especially useful when an enterprise needs to access and analyse massive amounts of unstructured data or data that’s stored remotely on multiple virtual servers in the cloud. NoSQL technology was originally created and used by Internet leaders such as Facebook, Google, Amazon and others, who required database management systems that could write and read data anywhere in the world, while scaling and delivering performance across massive data sets and millions of users. Benefits of NoSQL databases:   NoSQL databases provide various important advantages over traditional relational databases. A few core features of NoSQL are listed here,

Data Modelling is the diagrammatic representation showing how the entities are related to each other. It is the initial step towards database design. We first create the conceptual model, then logical model and finally move to the physical model. Generally, the data models are created in data analysis & design phase of software development life cycle. There are three types of data models – conceptual, logical and physical. The level of complexity and detail increases from conceptual to logical to a physical data model. Conceptual model will be just portraying entity names and entity relationships. Figure 1 shown in the later part of this article depicts a conceptual model. Logical model will be showing up entity names, entity relationships, attributes, primary keys and foreign keys in each entity. Figure 2 shown inside question#4 in this article depicts a logical model. Physical data model will be showing primary keys, foreign keys, table names, column names

Techniques to Boost Your Data Modeling : ·          Understand the Business Requirements and Results Needed - get a clear understanding of the requirements by asking people about the results they need from the data. Then start organizing your data with those ends in mind. ·          Visualize the Data to Be Modeled - Most people are far more comfortable looking at graphical representations of data that make it quick to see any anomalies or using intuitive drag-and-drop screen interfaces to rapidly inspect and join data tables. Data visualization approaches like these help you clean your data to make it complete, consistent, and free from error and redundancy. ·          Start with Simple Data Modeling and Extend Afterwards: Keeping data models small and simple at the start makes it easier to correct any problems or wrong turns. When you are sure your initial models are accurate and meaningful you can bring in more datasets, eliminating any inconsistencies as you go. ·