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…
- SQL – Examples: Oracle, MySQL, Microsoft SQL Server, PostgreSQL
- NoSQL – Examples: MongoDB, Redis, Casandra
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.
Most Popular Databases among Programmers
- Microsoft SQL Server
- Microsoft Access
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
3. Microsoft SQL Server
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
6. DB 2
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.
7. Microsoft Access
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.
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.