Can Next Generation Databases Keep Up With Big Data

Can Next Generation Databases Keep Up With Big Data


Next-generation databases, known as NoSQL databases, are rising to meet the challenges posed by handling large and complex data sources. Unlike traditional relational databases, NoSQL databases don't rely on structured schemas and offer faster data access for real-time applications. Divided into categories like wide-column stores, document stores, graph databases, key-value stores, and XML databases, NoSQL databases provide solutions for various data management needs. From handling machine-generated data to unstructured documents and graph relationships, these databases are essential for organizations dealing with the growing demands of Big Data.

Be it Oracle’s ORCL +0.00%, Microsoft’s MSFT +0.00% Access, or IBM’s IBM -0.61%’s DB2, these relational databases form a solid backbone for all kinds of data accumulation and storage needs. They are indispensable for structuring and accessing most forms of data; but then, they have their limitations when it comes to handling the complexities of extremely large-sized data sources. This is where a totally new category of the database comes to the rescue for handling Big Data challenges, and how!

Globally, organizations are looking towards next-generation databases for managing their ever-increasing bank of transactions and massive surges of data. Read on for a quick look into the nature of these new databases and how they are being leveraged for higher profitability.

How Next-generation Databases Differ from their Older Cousins

“NoSQL” (or “not-only SQL”) is the term given to these new kinds of databases that are being readily adopted for overcoming certain big data management challenges. They don’t require any highly-ordered plans (database schemas) for setting them up and refrain from using tables, fields and rows—the normal features of other relational databases. Presenting newer opportunities for data owners, NoSQL databases provide rapid data access for fuelling real-time applications as well. They streamline data in various non-traditional formats and bring down the turnaround time and costs required for developing conventional database schemes (also consider checking out this career guide for data science jobs).

The emergence of new Databases for Big Data Management

Today, if you ask yourself if you need a NoSQL database for managing your current levels of data, the answer will probably be ‘no’. However, the rate at which data is being stored, analyzed, used and forwarded in the existing scenario; the day is not far behind when you will have to resort to these upgraded databases. Under the circumstances, it makes good sense to gain familiarity with their different types, features, and the situations where they can be implemented in the best possible ways. 

New NoSQL databases are divisible into five major classes:

  • Wide-column stores/ columnar databases that are referred to as column families: 
  • Documents
  • Graphs
  • Key values
  • XML or ‘native XML’

Major Classes of Databases Compatible with Big Data

Column Families:

Close to conventional relational databases in certain ways, these NoSQL databases are capable of storing structured Big Data in the form of individual columns or groups of columns, rather than tables. Appropriate for handling rapid data queries, machine-generated data and big structured data sources that are unable to fit on single machines, they are capturing the attention of organizations requiring rapid precision analytics linked to their machine data.

Examples: Apache Hbase and Apache APA -1.82% Cassandra


More apt for the storage of unstructured documents (such as open textual content in letter or email), or semi-structured files (such as academic papers); these next-gen databases are being integrated for documented text analytics that cannot be stored conveniently on conventional databases.
Examples: Apache Couch DB and MongoDB


These databases are useful for managing Big Data that comes in the form of graphic representations, or as diagrams in relationships. They are now being used for powering web applications that are instrumental in providing quick and accurate references at lightning speed (think social networking platforms and e-commerce portals). These databases are necessary if you are planning to implement faster applications and are ready to deal with some approximations in analytics.

Examples: Microsoft’s Horton and Neo Technology’s Neo4J


Such databases are designed for applications that need fast application development processes. Here, key values gain prominence and are second to none (also consider checking out this perfect parcel of information for a data science degree). 

Example: Basho Technologies’ Redis and Riak


XML, the ‘language of the internet’, as well as a host of other information sharing systems, is used by this category of databases for defining data structure. Well-suited for Big Data in the format of audios or videos (essential for speech or video analytics), or those that are difficult to manage via other databases, these XML databases are certainly the way to go. 

Big names: Sedna and Mark Logic

If you are looking towards harnessing the challenges of Big Data, or seeking ways of making your applications perform like lightning, then it’s time to invest in these NoSQL databases—but not before having all your checks and balances in place!


Data science bootcamp

Join OdinSchool's Data Science Bootcamp

With Job Assistance

View Course