A database is must need for any software development and which database to choose is one of the main requirement for software architecture. In 2018 – 2019 year, As a developer we have lots of choices for the databases. We can have mainly two types of the database. So, let’s start with our most popular databases…

As above SQL have mainly relational databases and NoSQL means not only SQL databases. If you are not comfortable about NoSQL databases – like what is NoSQL database and types of NoSQL databases. I would recommend to read our latest article about NoSQL databases. So, Now we can go through our top 10 databases by the advantage and disadvantage of each. So, You will choose the right one for your application. Here we have a list of databases for the 2018 year.

Click here for Best NoSQL databases

Yes, Oracle is king in the race for most popular databases. Why? – its really famous among all developers, easy to use, well-written documents, amazing new features ( JSON from SQL, Robust Code using Constants for Data Type Lengths feature, long name support, list tag improved, etc).

The latest version of Oracle database is 12c.

Enterprises can commence out utilizing the free community server and later upgrade to the commercial version

Runs on Linux, Windows, OSX and FreeBSD and Solaris

Intuitive graphical utilizer interface for designing database tables

Due to its open-source community, MySQL has a sizably voluminous bank of tutorials and information to avail you get commenced and solve quandaries

Support for partitioning and replication, as well as for Xpath and for stored procedures, triggers and views

The most widely used commercial DBMS

Constrained to Windows, but this is an advantage if your enterprise uses mostly Microsoft products

A particularly scalable object-relational database

Runs on Linux, Windows, OSX and several other systems

Support for tablespaces, as well as for stored procedures, joins, views, triggers, etc.

The most popular NoSQL DB; nevertheless retains some SQL properties like query and index

Fortifies a wide range of programming languages like Scala, Groovy, Clojure, and Java – eminently more than NoSQL rival Cassandra

High performance on colossal databases

Best for dynamic queries and for defining indexes

Fortifies Linux, OSX, and Windows, but the DB size is circumscribed to 2.5 GB on 32bit systems

IBM’s answer to Oracle’s 11g, available in host and Windows/Linux versions

Runs on Linux, UNIX, Windows and mainframes

Ideal for IBM host environments

Support for both SQL and NoSQL data models.

Only one installation needed (DBMS and design implement in one)

Like Microsoft SQL Server, it’s use is circumscribed Windows

Ideal for getting commenced with traffic analysis, but not its performance is not designed for mid to astronomically immense-scale projects

Fortified programming languages inhibited to C, C#, C++, Java, VBA and Visual Rudimental.NET

Highly available NoSQL alternative to MongoDB

Subsidiary for storing particularly immensely colossal datasets with a utilizer-cordial interface

Popular in banking, finance, and logging, but withal utilized by Facebook and Twitter

Fortifies Windows, Linux, and OSX, as well as numerous languages

Map/reduce withal possible when utilized with Hadoop

It is open-source, networked, in-recollection, and stores keys with optional durability.

When the durability of data is not needed, the in-recollection nature of Redis sanctions it to perform astronomically well compared to database systems that indite every transmutation to disk afore considering a transaction committed.

Redis is commonly deployed on IaaS or PaaS platforms like Amazon Web Accommodations, Rackspace, or Heroku.

Many languages have Redis bindings, including: ActionScript, C, C++, C#, Clojure, Prevalent Lisp, Dart, Erlang, Go, Haskell, Haxe, Io, Java, JavaScript (Node.js), Lua, Objective-C, Perl, PHP, Pristine Data, Python, R, Ruby, Scala, Smalltalk and Tcl.

Elasticsearch is an open-source, broadly-distributable, readily-scalable, enterprise-grade search engine. Accessible through an extensive and elaborate API, Elasticsearch can power extremely fast searches that support your data discovery applications.

In Elasticsearch, these delicate and often intensive operations occur automatically and imperceptibly:

Partitioning your documents across an arrangement of distinct containers) In a multi-node cluster, distributing the documents to shards that resides across all of the nodes Balancing shards across all nodes in a cluster to evenly manage the indexing and search load With replication, duplicating each shard to provide data redundancy and failover Routing requests from any node in the cluster to specific nodes containing the specific data that you need Seamlessly adding and integrating new nodes as you find the need to increase the size of your cluster.

We are recently taking a survey from different programmers who are available in google plus social media. We did a survey for all SQL and NoSQL databases. You can see most popular database for 2018.

The post Top 10 Databases You Should Learn in 2018 appeared first on I'm Programmer.