Top 10 Enterprise Big Data Databases in Open-Source Realm

Big data is now changing ways as to how data is being handled in organizations and businesses make decisions. There are various types of databases and data warehousing technologies that now support the big data world. These databases help businesses manage vast stores of structured and unstructured data and make it possible to derive actionable insights from big data. There are several things that businesses can do if they accurately handle their business data. They need to ensure that they have the accurate data to get the best results.

Modern day businesses largely rely on open-source solutions from the basic tools like Cassandra to the highly integrated NoSQL DB of MongoDB. All these are designed to handle even the biggest of the data pools effectively. The number and variety of these tools are ever-increasing and can face the challenges of the changing business world. As a real-time example, you can take OrientDB, which can store about 150,000 documents each second. Modern-day organizations largely rely on such open-source databases and the users of the same range from Comcast to Boeing and even the governments.

As much as the tools and technologies in big data are, there are as many use cases also for these to handle. This article will list some key databases that play a central role in managing the global business marketplace for better coordination.

Top big data databases for now

This list is not made in ranking order. These are random choices for users to make based on their specific requirements and big data plans.

1. HBase

HBase is an Apache project, which is a reliable non-relational data store for the Hadoop applications. Features of HBase include modular and linear scalability, failover support with automation, highly consistent reads and writes, etc. The operating system is OS-dependent.

2. Cassandra

Facebook develops this DB. It is a NoSQL DB, which now comes under the Apache project. Cassandra is used by a lot of organizations that feature very large and active datasets. Some real-time examples of Cassandra databases are Twitter, Netflix, Constant Contact, Reddit, Urban Airship, Cisco, and Digg, etc. Support and service for this open-source database are given through third-party vendors. The operating system is fully OS independent and free to build on.

3. Neo4j

Neo4j is considered the most adopted graphic database in the world. This database boasts performance improvements of up to a thousand times or more versus the conventional relational DBs. It is operational on Windows and Linux operating systems. The users can purchase enterprise or advanced versions of the Neo4j databases based on their specific use cases. If you are confused about choosing the right DB for your big data store, you can consult with RemoteDBA.com

4. MongoDB

MongoDB is another frontline NoSQL database that can support high-volume humongous databases. This features a document-oriented storage model and guarantees high availability, full index support, replication, and more. The vendors like 10gen offer commercial support for MongoDB. It can work well on Linux, Windows, OS X, Solaris, etc.

5. CouchDB

CouchDB is a specialized database that is designed especially for the web. It can store big volume data as JSON documents which you can easily access anytime over the web and do the querying using JavaScript. This also offered a distributed scaling which has high fault tolerance in storage. The operating systems that support CouchDB are OS X, Linux, and Android, etc.

6. OrientDB

Another NoSQL DB can easily store up to 150,000 documents each second. It can also load graphics in a matter of milliseconds. This will combine the flexibility of the document database by using the power of graph databases and can support ACID transactions and fast indexes.

7. Territory

Terrastore is also an advanced DB for big data with high-end elasticity and scalability features without compromising consistency. It can also support custom data partitioning, push-down predicates, event processing, range queries, map-reduce querying, and also can process the server-side updates. This DB is operating system independent, and you can effectively use it for your big data applications without any limit.

8. FlockDB

This is a well-known DB, which powers the leading social media giant Twitter. FlockDB is designed to store the social graphs effectively. With this, you get the data as of the social connections as who is following and blocking any many such insights. FlockDB can also offer horizontal scalability and can ensure faster reads and writes. The operating system of FlockDB is OS Independent and can also be used for big data stores.

9. Hibari

This is a wonderful big data DB that is used effectively by many telecom providers. Hibari is a key-value-based DB, which can store big data with high consistency. The DB also ensures high availability and quick performance. Dedicated support is also available for Hibari through the vendor Gemini Mobile. It is an OS-independent database.

10. Riak

Riak is known to be one of the most powerful big data DB in the open-source sector. It is also a distributed database you can put into production. The major users of this database include Yammer, Comcast, Boeing, Voxer, Kiip.me, SEOMoz, DotCloud, Joyent, Formspring, etc. The Danish government is also using this database for administrative applications. It works on Linux and OS X-based operating systems.

When it comes to the database architecture to use for big data, you have many choices like document-oriented, columnar DBs, key-value DBs, graph DBs, etc. If you figure out which database to use for your project, you may further plan to structure the DB per the long-term scalability and usability needs. In many cases, regular databases or relations DBs also can be used as big data stores, but you need to consider these in light of your long-term needs too. The best advice in choosing an appropriate database for your enterprise big data application is to consult with a database expert and evaluate your specific use case to identify which one will work the best for you in the longer run.

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