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 efficiently, quickly, and securely. 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 and offering the best to their abilities. The goal of this article is to provide you with information regarding 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 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).

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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 needing to refresh the page. For further information on NoSQL, check out What you need to know about NoSQL databases.

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

  • Snowflake - 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.
  • 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.
  • 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.
  • MySQL - is an open-source RDBMS based on Structured Query Language (SQL). MySQL runs on virtually all platforms, including Windows, UNIX, and Linux.
  • Microsoft SQL Server - is an RDBMS database that supports a wide variety of analytics applications in corporate IT environments, transaction processing, and business intelligence.
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Other Database Options:

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.

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.

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.

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 in fraud prevention, advanced enterprise operations, or Facebook's original friends' graph.

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.

Take a look at some more recommendations by Forbes.

Strategize Before You Choose Your Database

Hopefully you have at least 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. You are not alone! The Zuar team has years of experience working with different database technologies, solving data challenges, and understanding cost to benefit of database systems. Contact us if you need help choosing the best option for your use case!

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