Analysts struggle to incorporate new sources of data into their analysis because they rely on Microsoft Excel or other tools that were not designed for data blending. Deleting columns, parsing data, and writing complicated formulas to clean and combine data every time it changes is not an efficient way for today’s analysts to spend their time.
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MongoDB is an open-source, document database designed with both scalability and developer agility in mind. MongoDB bridges the gap between key-value stores, which are fast and scalable, and relational databases, which have rich functionality. Instead of storing data in rows and columns as one would with a relational database, MongoDB stores JSON documents with dynamic schemas.
Customers should consider three primary factors when evaluating databases: technological fit, cost, and topline implications. MongoDB's flexible and scalable data model, robust feature set, and high-performance, high-availability architecture make it suitable for a wide range of database use cases. Given that in many cases relational databases may also be a technological fit, it is helpful to consider the relative costs of each solution when evaluating which database to adopt.
Relational database management systems (RDBMSs) are systems of software that manage databases as structured sets of tables containing rows and columns with references to one another through key values. They include the ability to optimize storage, process transactions, perform queries, and preserve the integrity of data structures. When used with applications, they provide the beating heart of the collection of business functions supported by those applications. They vary considerably in terms of the factors that impact the total cost of running a database application, yet users seldom perform a disciplined procedure to calculate such costs. Most users choose instead to remain with a single vendor's RDBMS and never visit the question of ongoing hardware, software, and staffing fees.
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