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JSON data can be stored as a column with optional indexes. In 9.4 (upcoming at the time of this writing), JSONB will be a binary version of JSON that will save space. It's like the best of the NO-SQL world without having to give up ACID and Relationships. This means that cascading deletes can be done in a single Transaction across multiple JSON documents. See More
PostgreSQL performance increases with each release, this is also proven by many benchmark tests. Notable performance features include: As PostgreSQL only supports one storage engine, it has been able to integrate and optimise it and with the rest of the database. This has resulted in multiple benefits such as the ability to allow different transaction types to co-exist efficiently without the need to select storage engine types once for each table ahead of time. On the fly data compression resulting in less IO required for reading. Asynchronous + synchronous Replication. PostgreSQL supports a asynchronous API for use by client applications. It is reported to increase performance by up to 40% and is not supported by MySQL. Designed to scale very well with large numbers of cores at high concurrency levels. See More
PostgreSQL is known to have a very holistic approach to robustness and data integrity which is reflected by it being fully ACID compliant. PostgreSQL has always been strict about making sure data is valid before allowing it into the database, and there is no way for a client to bypass those checks. See More
MongoDB has powerful sharding and scaling capabilities for when the data stored in the database gets so large that a single machine may not be able to store all of it. Sharding solves this problem through horizontal scaling. Mongo gives developers the ability to easily and painlessly add or remove as many machines as needed. See More
MongoDB queries can be very fast because the data is usually all in one place and can easily be retrieved in a single lookup. But this is true only when the data is truly a document. When it's trying to emulate a relational model it starts to become really slow because it may have to perform many independent queries to retrieve a single document. See More
Since CouchDB is considered an AP (Available, Partition-Tolerant database management system), it is not really consistent (not all clients can have the same view of the data consistently) and the only way to achieve some "eventual consistency" is through replication and verification of data. See More
The two fatal flaws of many open source solutions is lack of integrated security at the element level and full ACID transaction support. MarkLogic has both and is trusted with some of the worlds most sensitive data. It is the engine that powers healthcare.gov, which despite some early problems (not caused by MarkLogic) is an amazing technological achievement. See More
MarkLogic can be downloaded and implemented in development environments for free. However, for production use, it is priced for enterprises, not startups with tight budgets. Open source requires a lot more elbow grease to do the same thing. See More
MarkLogic supports text, documents, key-value/tuple, graphs, tables and object models that don't require extensive data-modeling and normalization that is part of the lifecycle process of relational database management systems. For sites similar to LinkedIn, Facebook, IMDB and even search engines, MarkLogic provides a unique set of features that are all in one box. See More
MarkLogic can store all your enterprise data in it's original format without needing to know a schema in advanced. You can shove pretty much any structured or unstructured data directly into MarkLogic, and it will automatically index everything and make it available for future processing. Of course it is fully schema aware and will apply and enforce schema constraints when available, but the tedious normalization that is required for relational databases is not necessary. See More
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