Choosing an Enterprise Project’s DataBase? Simplified.

Updated: Apr 26

There are a huge number of choices, a huge number of benefits, competitive communities & multiple use case scenarios. Finding out the best possible match database in Graph-Based or in Column-Based or it is Document-Based, it is never easy. Every category has a list of different options to choose from with a different number of advantages or disadvantages. In this post, we will try to understand the advantages, disadvantages, examples & use cases for most of the popular available databases.

Three main keys to consider while choosing a database

  • Availability

  • Consistency

  • Partition Tolerance

According to the CAP theorem (Brewer’s theorem), when you are designing a distributed system you can get cannot achieve all three of Consistency, Availability and Partition tolerance. You can pick only two out of above mentioned three. ~ wiki

Let’s get into the practicality of selection. We have divided No-SQL Databases into 4 categories.

  • Key-Value

  • Document-Based

  • Column-Based

  • Graph-Based

Categorized Database Names:

Key Value: Riak, Redis Server, Memcached, Scalaris, Tokyo Cabinet. Document-Based: MongoDB, CouchDB, OrientDB, RavenDB. Column-Based: Cassandra, Hbase, Hypertable, BigTable. Graph-Based: Neo4J, InfoGrid, Infinite Graph, Flock DB

Apart from above, there is something more called, Multi Model Database. We’re going into it after covering these above 4.


It is faster, but it’s schema-less (unstructured). Examples: Url Shortner, PasteBin, E-commerce- in use cases for, temporary prices, user profiles, product recommendations, session information etc. Companies using: Twitter uses Redis to deliver your timeline. Pinterest uses for followers, following, etc.


Designed for storing, retrieving and managing document-based information. Advantages: Data Tolerant Disadvantages: Query Performance, no structured query. Use Cases: Can be used as scalable general purposes. Example: A famous weather app (iOS), delivers weather alerts to 40M users, SEGA uses MongoDB for handling 11M in-game accounts.

Column Based

Offer very high performance and highly scalable architecture, because it is fast to load data and query it. Excellent real-time usages: – Tweet information of a user is saved as column-wise – Organizes the data into rows and groups of columns. – Facebook: uses Column-based for nearby friends (Hbase). – Spotify: uses Cassandra to store user profile attributes like artists, songs etc.


Graph-based is used for various purposes and used by many good companies. – LinkedIn- For showing connections – Google Knowledge Graph– For example, search for — Indian Prime Minister and the first result box given by google is an example of graph-based. – Walmart– uses Ne04J for customers' personalized product recommendations. – Medium– uses Neo4J to build their social graph to enhance content personalization.

The below picture depicts where and when you can utilize it.


Source: Martin Fowler

Multi-Model Database

A Multi-Model Database combines the capabilities of Column-Based, Graph-Based, Document Based and Key-Value Databases.

Example: Microsoft Azure Cosmos DB, Orient DB

The Monolithic Database Approach

The Monolithic Database Approach

Issues in the monolithic approach

  • Difficult to make schema changes

  • Vertical scaling

  • Single point of failure

  • Technology lock-in

Let’s split around this monolithic approach to resolving issues

Split Around Monolithic Approach

Data Categorization

Transient Data:

The information generated from the application/system.

1- Events, logs, signals 2- No persistent storage so it should be highly available

Ephemeral Data

Temporary data whose sole purpose is to improve the user experience by serving information in real-time e.g. cache for user experience.

Operational Data

Information gathered from user sessions — such as user clicks, cart data.

Transactional Data

Payment processing and order processing data.

I hope this is somewhere useful for you. Let me know your views.

Thanks for reading. :)


Subscribe to Our Newsletter

© 2023 by BLIO

  • YouTube
  • Instagram
  • reddit-logo-newnew
  • medium-logo
  • LinkedIn
  • github-logo-new
  • Twitter
  • tiktok-logo-newnew
  • Facebook