NoSQL Databases for Startup Businesses - An Overview

Considering NoSQL Databases for Startup Businesses – An Overview

Date: 06-Mar-2019

Enterprises database practices were lingering around the concept of relational databases management systems and SQL platforms for long. But, in the last few years, we have seen the rise of a new genre of databases which is known as NoSQL databases or Not only SQL database management systems. Now, these NoSQL platforms are challenging the unquestioned dominance of the conventional relational databases, which existed so for more than a couple of decades.

Relational databases were very strict and stringent in terms of its mechanisms to store data, structuring it, transactions, and concurrency control and on the standards and mechanisms for integration of application data and also reporting. This dominance is, however, cracking now. For startups and small-scale businesses, NoSQL solutions were very troublesome to get accustomed to and also the scaling up of relational databases was so costly. Now, NoSQL seems to be a much affordable and convenient option for the startups too to adopt and easily scale up based on their needs.
What is NoSQL?
NoSQL means Not only SQL, which further implies that while you are planning to design a product of web application, but there are also many storage mechanisms which you can make use of. NoSQL was actually a hashtag, I.e., #nosql, which was chosen for a tech meet-up to discuss the new generation databases. The major advantage with the rise of NoSQL is considered to be the Polyglot Persistence.

There is no descriptive definition for NoSQL, but the common observations about this concept are like:

  • Databases which don’t use a conventional relational model
  • The open source database model
  • Running on clusters
  • Schema-less
  • Custom built for the web estates of the 21st century

Why NoSQL databases for startups?
We can see how the application developers are now largely frustrated with the mismatch between the in-memory and relational data structures of any given application. By using NoSQL database solutions, the developers can now more comfortably develop applications without the need to convert the in-memory structures to be compliant with the relational structure. There is also a movement towards using the databases as ideal integration points to encapsulate the databases in applications
and integrate it as a service.

The rise of concepts like the web as a platform and infrastructure as a service everything also contributed towards a vital change in terms of database administration with the need to support huge volumes of data run on clusters. The old-age relational databases were not ideal for running on clusters efficiently. Considering the old age enterprise web applications against that of the new-age enterprises, we can see that the storage need  of a general ERP application is totally different compared to the enormous data storage and management needs of Amazon, Facebook, or Etsy, etc. The relational DBMS is also largely different than data structures which are used by application developers now. Using data structures custom modeled by the developers to deal with different problems now had given rise to a different approach than relational modeling, as aggregate models. Most of the part of this model is towards the concept of the Domain-Driven Design, a book written by Eric Evans. The aggregate is considered to be a data collection which will interact with a unit. These aggregates or the units of data
form the ACID operational boundaries in database structures like document, key-value, and column-family databases.

Aggregate databases also make handling of inter-aggregate relations more difficult. The aggregate-ignorant DBS would prove out to be better in terms of interactions and use the data which is organized in varying formations. Databases in aggregate-oriented format may often compute the materialized views in order to provide the data organized in a different manner than the primary aggregates. It is usually done with Map Reduce computation.

Various distribution models

Aggregate-oriented DBs also make the data distribution much easier since the mechanism of distribution in this mode need not have to worry about the related data as all the data gets contained in aggregate. Two styles of data distribution in this model are:

  • Sharding – Distributing data across different servers so that every server in the cluster will act as a single source of the data subset.
  • Replication – Preparing multiple copies of the data across various servers, so that every bit of data is stored at multiple places.

There are two forms of replications as:

  • Master-slave replication – In this one node in the cluster is made the authoritative copy which enables writes and different slaves are created which get synchronized with the master copy and handle only reads.
  • Peer-to-peer replication – This allows writes on any node, which then synchronizes with the other copies and gets updated.

The concept of master-slave data replication will reduce chances of any conflicts in terms of updates, but in peer-to-peer model, it can avoid loading of all writes on to one server and thereby having a single point of failure. You can use either systems or both for your purpose.

Choosing an ideal NoSQL database for startups

Given many choices, it’s not easy for you to choose an appropriate database model for your startup business. To make this easier, here we will discuss a few expert inputs in this regard.

  • The key-value database is primarily useful to store;
  • Session information
  • User profiles
  • Customer preferences
  • Data about shopping cart etc.

It may be worthwhile to avoid usage of the key-value database while the need is to query by the data and to store the relationships between data.

  • Document databases can be used at best for;
  • CMS systems
  • Blogging sites
  • Web analytics applications
  • Real-time business analytics needs
  • E-com applications etc.

It is advisable to avoid document databases for applications with complex transactions like running multiple operations or simultaneous queries across varying structures.

  • The column family databases can be used at best for;
  • Content management systems
  • Blogs
  • Heavy write volume applications etc.

However, we may not use column family DBs for systems which are in the early stage of development with the need not change query patterns.

Graph databases are ideal for problem spaces with highly inter-connected data as:

  • Social networks
  • Spatial applications
  • Routing info for money and goods
  • Recommendation engines etc.

For those who are totally novice in database administration sector, it is ideal for getting
expert advice on choosing an appropriate database model for your startup business database set up.


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