RDBMS - Relational Database Management System (i.e. SQL databases)
NoSQL - Non-Relational Database Management System

  • SQL databases are relational, NoSQL are non-relational.
  • SQL databases use structured query language and have a predefined schema. NoSQL databases have dynamic schemas for unstructured data.
  • SQL databases are vertically scalable, NoSQL databases are horizontally scalable.
  • SQL databases are table based, while NoSQL databases are document, key-value, graph or wide-column stores.
  • SQL databases are better for multi-row transactions, NoSQL are better for unstructured data like documents or JSON.


RDBMS have been an established standard in the industry for nearly 40 years using the programming language SQL. RDBMS are table based and follow a highly organized and structured approach to data management (think Excel). And apply strict category parameters to allow users to organise, access and manage their data within those parameters. SQL databases are vertically scalable, which means that you can increase the load on a single server by increasing things like CPU, RAM or SSD.


  • They are highly stable and reliable.
  • They adhere to an industry recognized standard (with Tech-Stacks like LAMP)
  • Because they’ve been around for so long, support options are abundant.
  • ACID compliance. If a database system is “ACID compliant,” it satisfies a set of priorities that measure the atomicity, consistency, isolation, and durability of database systems. The more ACID-compliant a database is, the more it serves to guarantee the validity of database transactions, reduce anomalies, safeguard data integrity, and create stable database systems. Generally, SQL-based RDBMSs achieve a high level of ACID compliance.


  • Scalability challenges,
  • These databases also present challenges when it comes to sharding. Sharding is the process of dividing a large database into smaller parts for easier management.
  • Less efficient with NoSQL formats: Most RDBMSs are now compatible with NoSQL data formats, but they don’t work with them as efficiently as non-relational databases.

Use Cases

  • Ideal for consistent data systems, your information will remain in the structure you originally create.
  • If you don’t need a dynamic information system for massive amounts of data—and you’re not dealing with numerous data types an RDBMS offers great speed and stability.

If you’re dealing with a conservative database that you don’t expect to change a lot in the years ahead, the sharding and scaling challenges related to RDBMS solutions may never apply to you. On the other hand, if you plan to scale up and grow in the years ahead, a non-relational database system (NoSQL-based) could be a better match for your needs.



Open Source, frequent security and features updates. (Paid versions exist for enterprise). Mature, pre-defined structure and set schemas. High compatibility. Good for use cases that require multi-row transactions like accounting or inventory management.


PostgreSQL is a hybrid SQL/NoSQL database system that finds a middle-ground between these two options - Open Source, highly compatible, Highly ACID Compliant, Massive Scalability. Object Oriented Database Management, excellent when your data doesn’t mesh well with a perfectly relational model. PostgreSQL is highly trusted and supported (it’s been around since the early 1990s). It works well with extra-large databases and for performing complicated queries.


NoSQL databases are built to handle large amounts of unstructured data such as email, text, surveys, social media etc. Data schemes are only loosely defined and easy to modify or add to where required. (Similar to the file system on your PC). NoSQL databases give up the distinction of ACID compliance to gain speed and flexibility when dealing with unstructured data. NoSQL databases are horizontally scalable, this means that you handle more traffic by sharding, or adding more servers in your NoSQL database.


  • Excellent for handling “big data” analytics. They remove bottlenecks of needing to categorize data and apply structures before analyzing it.
  • Easy to scale, they are designed to be fragmented (across data-centers for example)
  • No limits on data types that can be stored.
  • You can store unstructured information and expose it to powerful business intelligence systems to analyse it.
  • You can store unstructured data that you may want to structure later.
  • Less up front data prep.
  • Non-relational databases work with formats like JSON for web-based applications that let websites update “live” without needing to refresh the page.


  • As NoSQL is relatively new there may be less opportunities to find support.
  • Less tools available.
  • There may be compatibility with older RDBMS systems.

Use Cases

  • NoSQL databases the preferred choice for large or ever-changing data sets.


Open Source, Dynamic Schema, Highly Scalable, Manageable and high speed. MongoDB is a good choice for businesses that have rapid growth or databases with no clear schema definitions or if you find yourself denormalizing data schemas. Or if your data requirements and schemas are constantly evolving - as is often the case with mobile apps, real-time analytics and content management systems, etc. Perfect for building an application on top of an operational database and you need a really fast response time.



Open Source, originally created by Facebook but ownership has been transferred to Apache. Active everywhere, users can write and read from all Cassandra nodes. Highly scalable. Cassandra benefits from a “masterless design.” That means all of its nodes are identical, which creates operational simplicity, making it easy to scale up to a larger database architecture. Support for SQL. Offers excellent data protection. Note, Cassandra is not optimized for updating and deleting data.**

Cassandra is most popular for use with IoT (internet of things) technology because it offers fast, real-time insights and excels at writing time-based log activities, error logging, and sensor data. If you need fast read and write processing, Cassandra could be your database. Cassandra is also good for those who want to work with SQL-like data types on a NoSQL database.



Google owned paid service. Sub 10ms latency guaranteed. Replication. Easy to integrate, highly scalable. Highly compatible with Google services. Fully managed with Integrations which reduces workload requirements. It also integrates instantly with many platforms, which streamlines the ETL processes required to load data.

Machine learning: BigTable features a storage engine for use with machine learning applications. BigTable can also be used for fin-tech, IoT, and advertising technology

Apache HBase

Open Source, modeled after Google’s BigTable. Created to work with large data sets. Excellent at scaling across a cluster. HBase organizes rows into “regions.” The regions determine how the table will be divided across more than one node that make up a cluster. If one of the regions is too big, HBase automatically breaks it up to evenly distribute the load across more than one server. Works with both unstructured and semi-structured data. The Apache HBase website advises to use HBase “when you need random, realtime read/write access to your big data.” The database is designed to host massive tables of information that include billions of rows and millions of columns.

How to Chose the Right Database

There are many differing factors that come into play when choosing a database for your project but 3 big factors may be:

  • Atomicity. If atomicity is a top priority for you, stick to a relational database.
  • Scalability (and sharding).
  • Speed. If speed is more important than ACID compliance, a non-relational database, such as a document database, is a better bet.