It's the wild west out there, and you’d need to build a system capable of tracking every version and format of every document in your database to ensure that nothing is missed-a risky proposition. The variable nature of MongoDB's document-based data extends that in-house process considerably. Already it can take days or weeks to build useful, safe test data in-house using a standard RDBMS. If that granularity isn’t achieved, the resulting test data will, at best, fail to accurately represent your production data and, at worst, leak PII into your lower environments.Ī high degree of privacy paired with a high degree of utility is the gold standard when generating test data based on existing data. A high level of granularity is required to ensure data privacy when generating test data based on this data, whether through de-identification or synthesis. What’s more, it is often in the form of nested arrays that create complex hierarchies. Data of this type can come in the form of physician notes, job descriptions, customer ratings, and other formats that aren't easy to quantify and structure. JSON documents have great utility because they can be used to store many types of unstructured data from healthcare records to personal profiles to drug test results. The JSON file format itself introduces its own level of complexity. Where in one document, a field may contain a string, that same field in another document may have an integer. The elements of each document can develop and change without requiring conformity to the original documents, and their overall structure can vary. Unlike traditional RDBMS platforms with predefined schemas, MongoDB functions through JSON-like documents that are self-contained with their own individual definitions. While the ease of creating documents to store data in MongoDB is valuable for development purposes, it entails significant challenges when attempting to create realistic test data for Mongo. Its agility results from intelligent indexing, sharding across multiple machines, and workload isolation with read-only secondary nodes. MongoDB's scalability can be attributed to its ability to define clusters with hundreds of nodes and millions of documents. MongoDB was born from their desire to create something better.Īs of this writing, MongoDB ranks first on for documents stores and fifth for overall RDBMS platforms.īeing document-based, Mongo stores data in JSON-like documents of varying sizes that mimic how developers construct classes and objects. They were frustrated with the difficulty of using existing database platforms to develop the applications they needed. Ryan, and Eliot Horowitz - were founders and engineers at DoubleClick. Before MongoDB's inception, its founders - Dwight Merriman, Kevin P. MongoDB was built with the intent to disrupt the database market by creating a platform that would ease the development process, scale faster, and offer greater agility than a standard RDBMS. In 2013, 10gen changed their name to MongoDB to unify the company with their flagship product, and the company went public. It is an open source database with options for free, enterprise, or fully managed Atlas cloud licenses.ĭevelopment on MongoDB began as early as 2007 with plans to release a platform as a service (PaaS) product however, the founding software company 10gen decided instead to pursue an open source model. It derives its name from the word 'humongous' - 'mongo' for short.
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