With the extensive SQL support and migration tools, Postgres users are easily able to migrate their databases over to the Postgres database with little effort.
JSON support allows for Postgres to transfer data as raw text and without attributes and markup overhead. This will result in new levels of speed, efficiency and flexibility for developers.
You can reuse existing approved design table and extend its capability by inheritance. You can reuse ancestor method byy declaring new trigger for descendant but reuse ancestor trigger procedure. You can add, remove or override column constraint making it differ from its ancestor. Applying OOP with database design help me reuse, extend, and encapsulate business logic and end up a rapid design, and less errants. PostgreSQL is a powerful RDBMS while its OOP is sufficient for OOP modular design. Especially its Open Source ,and free.
Postgres uses MVCC. For each user connected to the database the Postgres database shows a snapshot of the database at a particular instance. When the database needs to update an item it will add the newer version and mark the old version as obsolete. This allows the database to save overhead but requires a regulated sweep to delete the old, obsolete data.
SQLite is made to be extremely lightweight and portable, but it still uses SQL. However, some SQL features such as RIGHT OUTER JOIN and FOR EACH STATEMENT are missing.
An SQLite database is a single ordinary disk file that can be located anywhere in the directory hierarchy. It works by sending requests to a single file where all the data is kept instead of communicating with a hosted database which gives access to an interface by making use of sockets and ports. The file format used is also cross-platform, so can easily be migrated to various machines.
This makes SQLite extremely portable throughout different applications, all that's needed to transfer the whole database is to make a copy of the file.
There is literally no configuration required to get SQL lite up and running. This is mainly due to SQLite being serverless, there is no separate server process to install, setup, configure, initialize, manage, and troubleshoot.
SQLite has bindings for a large number of programming languages, including BASIC, Delphi, C, C#, C++, Clipper//Harbour, Common Lisp, Curl, D, Free Pascal, Haskell, Java, JavaScript, Livecode, Lua, newLisp, Objective-C (on OS X and iOS), OCaml, Perl, PHP, Pike, PureBasic, Python, R, REALbasic, REBOL, Ruby, Scheme, Smalltalk, Tcl, Visual Basic.
Some of these APIs include ODBC, Java (JDBC), Perl, Python, PHP, Ruby, and native C. Also, there are a number of third party APIs that are available for use to interact with the database
Some of the most important features are the partitioning option, active data guard, and security options. All of these are very useful for daily workflow.
Oracle is very (in)famous for being involved in a number of controversies. Some of them have ended up in lawsuits with other tech companies because of license disputes. The latest one is a lawsuit against Google for Google's implementation of JAVA in their Android ecosystem.
Oracle Database has introduced concurrent execution of union branches, which means that one set of parallel servers will execute one branch, a second set will execute another branch and so on. Instead of the old system where each branch executed one after the other.
To work around ghost data you can after encrypting something, manually move everything out of the old table space and then shred those data files. To be able to get rid of ghost data automatically would be great.
This database is primarily, or historically, thought of as a graph database. However, it is actually a multi-model database that supports a variety of noSQL models (key-value, document, etc.).
A lot of effort went into making OrientDB easy and familiar to use (such as SQL queries for example). However, it's important to understand the database engine a bit and how it works in general (links for example). This involves a bit of a learning curve if you're not familiar with graph databases. Not an enormous curve or anything, but it's still a consideration.
It's a very unfortunate thing, but it's true. Not many people know of this gem so that makes it difficult to find community support at times. It also makes it hard to get people on board with it who shop products by brand name or flashy marketing. It also means there are less code snippets, libraries, and frameworks that consider it. While it's interoperability is good with SQL support and Tinkerpop, it still doesn't change the fact that many things don't give it much thought and that can lead to more work as a developer sometimes.
SQL Server Express has deep integration with Visual Studio, Visual Web Developer and SQL Server Management allowing for ease-of-use and rich table and query designers. It may be the best choice for someone who develops in the Microsoft environment.
"Manage SQL Server Express databases with SQL Server Management Studio Express. Connect to local SQL Server Express databases and manage objects with full Object Explorer integration. Write, execute, and test queries by using visual query plans, and access management and maintenance options."
Maximum number of cores: 4
Maximum memory utilized per instance: 1GB
Maximum size: 10GB
This hardware could be limiting with some high profile apps and sites.
With a variety of versions to choose from the SQL Server Express version is free of charge. It is capable of upgrading to higher tiers, but that requires a fee.
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.
As Node.js uses JavaScript there's no need to map the returned JSON data from MongoDB, as JavaScript is a superset of JSON. Essentially solving object-relational impedance mismatch by its very nature. Working with JSON is also easier overall as it more easily fits into how you would represent data on the client.
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.
No other ACID database has even close to the text handling capabilities that are built-in to MarkLogic. Many multi-model databases rely on Lucene to provide text indexing, which leads to unnecessary latency and complexity.
MarkLogic is more focused on the specific needs of enterprise customers who need ACID rigor and element level security. There are less costly options if eventual consistency and application-level security are good enough for you.
Configuring MarkLogic for scale out is simple with both APIs and UI based mechanisms to add and manage cluster nodes that auto-shard and redistribute themselves for maximum efficiency.
MarkLogic supports XML natively and adheres to all W3C standards surrounding XML. It even uses XQuery as a powerful application engine. Recent editions are also JSON native, which makes it fit in with modern JavaScript and web-centric applications.
If you are skeptical about product claims, MarkLogic may seem a bit too good to be true. MANY applications could benefit from implementing MarkLogic instead of creaky old RDBMS or immature NoSQL databases or even search engines like Lucene/Solr/Elastic.
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