The Types of Modern Databases

Aren’t sure which modern database is right for your organization? Zuar explores the different database models and their benefits.

The Types of Modern Databases

Are you running a digital content management system or handling configuration data? Possibly storing data from IoT devices or transaction information or recording inventory? Or are you dealing with any other system that generates data or handles data? If any of your data needs to be accessed and stored, you will need a database.

First, what is a database? A database allows several users to update, edit, maintain, and store information at the same time. This makes using a database purposeful for storing vast amounts of data from a network IoT devices, building a web application, keeping track of corporate accounting records, or tracking your company's inventory systems.

There are various types of modern databases available, each unique in their own way. The goal of this article is to provide you with information regarding the different types of modern databases.

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How Do You Choose a Database?

You are probably asking yourself , "how do I know which database to choose for myself and my company?" The answer depends on a number of factors including your intended use for your data like Business Intelligence, data structural requirements, data type, performance, data volume, and data model requirements. Other factors to consider are the querying mechanism, latency or data freshness, and consistency.

So, where do you begin choosing a database? Before you jump directly into purchasing modern database options, you should first understand the different types of databases:

  • Hierarchical: A hierarchical database utilizes a parent-child relationship to
    organize and manage data in a tree-like structure. In this type of database, the
    schema of a hierarchy has a single root.
  • Network: Like a hierarchical database, a network database enables a child
    record to link to several parent records, allowing multi-directional relationships.
    This allows several records to link to the same owner file.
  • Object-Oriented: Object-Oriented Databases (OODBs) are used for complex
    data structures to reduce overhead and flatten the object for storage. The
    structure of the objects determines the relationships between objects.
  • Relational: A relational database structures the data as a two-dimensional array. The data is placed into tables and organized by rows and columns. Relational databases use keys within a column to order and create relationships to other tables.
  • Non-relational: A non-relational database doesn’t use a tabular schema that
    most database systems use. Instead, it utilizes numerous formats for database
    design, enabling more flexibility and scalability in the design to accommodate
    different data types.

Of these database models, most organizations use a relational and non-relational database. So what are the difference between a relational database management system (such as RDBMS, i.e., SQL databases) and a non-relational database management system (such as NoSQL databases)?

Types of Modern Databases

While there are various relational databases, over some time, these are the ones that became the most well known:


Snowflake is an analytic data warehouse provided as Software-as-a-Service (SaaS). The Snowflake data warehouse uses a new SQL database engine with a unique architecture designed for the cloud.

To the user, Snowflake has many similarities to other enterprise data warehouses, but also has additional functionality and unique capabilities. Snowflake is a turn-key solution for managing:

  • Data engineering
  • Data lake
  • Data warehouse
  • Data science
  • Data applications
  • Data sharing

Snowflake provides one platform that can integrate, manage, and collaborate securely across different workloads. It also scales with your business and can run seamlessly across multiple clouds.

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Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers.

Amazon Redshift is a cost-effective solution for:

  • Running high-performance queries to improve business intelligence.
  • Generating real-time operational analytics on events, applications, and systems.
  • Sharing data securely inside and outside the organization.
  • Creating predictive analytics of the data in your data warehouse.


PostgreSQL is usually referred to as Postgres, is an object-relational database management system (ORDBMS) emphasizing standards compliance and extensibility.

PostgreSQL has earned a strong reputation for its proven architecture, reliability, data integrity, robust feature set, extensibility, and the dedication of the open source community behind the software to consistently deliver performant and innovative solutions.

There are several benefits of utilizing a PostgreSQL database. It is a free, open-source, extensible database that:

  • Defines data types based on custom configuration.
  • Allows you to program in different languages without recompiling and build
    custom functions.
  • Protects data integrity by creating fault-tolerant environments.
  • Enables developers to build applications that can manage data across varying
    sizes of datasets.


MySQL is a free, open-source RDBMS based on Structured Query Language (SQL). MySQL runs on virtually all platforms, including Windows, UNIX, and Linux. A popular database model that is used globally, MySQL offers several benefits:

  • Can securely and reliably manage high volumes of data
  • Complete customization that can scale with your business
  • Distinct storage engine that facilitates high performance
  • 24/7 uptime with a range of high availability solutions
  • Comprehensive support for transactions

Microsoft SQL Server

Microsoft SQL Server is an RDBMS database that supports a wide variety of analytics applications in corporate IT environments, transaction processing, and business intelligence.

Organizations can implement a Microsoft SQL Server on-premise or in the cloud

depending on structural needs. It can also run on Windows, Linux, or Docker systems. As part of the Microsoft toolkit, many Microsoft’s applications and software integrate well with Microsoft SQL Server.

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Other Database Options:

If you need a more specialized database, you may want to check out these other database options:

Key-Value Stores

Key-Value Stores, such as Amazon DynamoDB and Redis, are straightforward database management systems that produce fundamental services for retrieving the value associated with a known key and store only key-value pairs.

The easiness of key-value stores makes these database management systems particularly well-suited to embedded databases. The stored data is not particularly complicated, and speed is of prevalent significance. The most significant advantage of a Key-Value Stores database is speed, scalability, and flexibility.

Wide Column Stores

Wide Column Stores, such as HBase, Scylla, and Cassandra, are all schema-agnostic systems that enable users to store data in tables or column families, or a single row that can be thought of as a record which is a multi-dimensional key-value store.

These types of solutions are designed with the intent of scaling well efficiently to manage the petabytes of information across as many as thousands of commodity servers in a distributed and extensive system.

Even though technically speaking schema-free, wide column stores like Cassandra and Scylla use an SQL variant called CQL for data manipulation and definition, allowing them to be straightforward to those already accustomed to RDBMS.

Wide Column Stores are best suited for organizations that need:

  • Data extraction that utilizes row keys
  • High write speeds
  • Massive data volume

Document Stores

Document Stores includes Couchbase and MongoDB, are schema-free systems that accumulate data in the form of JSON documents. Document stores are similar to wide column stores or key value, but the document's name is the key, and the contents of the document are it's worth.

In a document store, the individual records do not require a uniform structure, contain many different value types, and are nested. This flexibility allows them to be particularly suited to manage semi-structured data across distributed systems.

Document Stores databases are best suited for applications that require:

  • Frequent changes to your data schema
  • Computed or aggregated data
  • Efficient pagination of query results

Document Stores are tree data structures that act as indices.

Graph Databases

Graph Databases, such as Datastax Enterprise Graph and Neo4J, represent data as a network of related nodes or objects to facilitate graph analytics and data visualizations. An object or node in a graph database contains free form data that is connected by group according to labels and relationships.

Graph-Oriented Database Management Systems (DBMS) software is created with the emphasis of demonstrating associations between data points. Therefore, resulting as graph databases that are typically used when analyzing the relationships between different data points is the system's end goal, such as:

  • Fraud prevention
  • Advanced enterprise operations
  • Facebook's original friends' graph.

Search Engines

Search Engines, such as Solr, Splunk, and Elasticsearch store data using schema-free JSON documents. These are very similar to document stores, but with a higher emphasis on making your semi-structured or unstructured data easily accessible via text-based searches with varying complexity strings.

There are several benefits for using a search engine database:

  • Data exploration and analysis without coding requirements
  • Generate training, testing, and validation data splits faster and cheaper
  • Can custom map and shape a variety of data and content types
  • Perform search-driven analytics

For data scientists, search engine databases provide the most useable analytics and

integrate well with most applications and tools.

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Of these database models, most organizations use a relational and non-relational database. So what are the difference between a relational database management system (such as RDBMS, i.e., SQL databases) and a non-relational database management system (such as NoSQL databases)?

Database admin software

Relational Database Management Systems (also referred to as SQL-Based Systems)

Relational database management systems (RDBMSs) use SQL, a database management language that offers a highly organized and structured approach to information management. It works similarly to how a phone book has different categories of information such as number, name, address, etc. for each line of data, relational databases apply strict, categorical parameters that allow database users to organize, access quickly, and sustain information in those parameters.

Relational Databases continue to be a top choice among those looking for a place to store their data. Some reasons why SQL-based RDBMSs continue to outshine other databases systems is because:

  1. They are highly stable and reliable.
  2. They support a standard that integrates seamlessly with popular software stacks like LAMP.
  3. They are commonly known, have a strong track record, have been in use for more than 40 years.
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Non-Relational Database Systems (also referred to as NoSQL-based system)

Envision that you're assigned to manage large amounts of unstructured data, such as customer surveys and text from emails, data collected by random social media information, or a network of mobile apps. The information collected is unorganized, and there isn't a clear, well-defined schema, like the one you would find in an RDBMS. Unfortunately, you are not able to store the information in an RDBMS. However, you can store it within a non-relational (or NoSQL) database system.

Non-relational databases allow you to organize information in a more relaxed fashion, like dropping the data in different file folders. This is important for two reasons:

  1. You are able to store unstructured information and expose it to powerful business intelligence systems that will analyze it with AI algorithms.
  2. You are able to store unstructured data that you plan to structure later.

Non-relational databases also work with NoSQL formats like JSON, which has become essential for web-based applications that let websites update 'live' without the need to refresh the page.

Strategize Before You Choose Your Database

Now you have a high-level understanding of different types of databases. We at Zuar understand that there are a variety of technical challenges and nuances to consider when selecting a modern database technology. The Zuar team has years of experience working with different database technologies, solving the data challenges of our clients. Contact us if you need help choosing the best option for your specific use case, or for a free data strategy assessment!

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