Suppose you are storing data from IoT devices or using a content management system. In that case, you may have a constantly increasing need for handling and configuring data to record inventories or transactional information, etc. If you need your information to be stored and accessed as and when wanted, you should have a solid database system in place.
However, when you look a look at the database landscape, it is possible that you may get surprised to see the diversity of choices of databases and database management technologies out there. Along with the so-called monolithic RDBMS systems, which stay there for a long, acceptance of non-relational databases too is on the rise.
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Relational vs. non-relational DBs
Based on the data type, model, structure, and intended use cases, various systems are available to best suit your needs. Based on your schema and querying mechanisms, consistency and latency requirements, or the need for transactional speed, you can choose among various types of highly featured databases. For example, the requirement for an embedded database for a system with dynamically configured data for a local application may be far different from the requirements for an operational database meant for real-time financial transactions as like in banking.
So, where to begin while choosing an apt database is the major question enterprise decision-makers may face while thinking of an apt database. Let us look into relational and nonrelational database management systems to get a better insight into them.
Relational databases or SQL-based DBS
Relational database management systems are also called SQL-based databases, which are widely used than their NoSQL counterparts. Relational DBMS have emerged back in the early ’70s, which were meant to store enterprise data based on the set schemas by allowing it to be stored in tables and rows. You may see the structure of relational databases as a collection of data tables, each following a schema representing fixed data types and attributes. Relational databases offer the functionality of writing, reading, updating, and deletion of data by means of SQL queries.
Relational database tables have keys associated with them, which can be used to identify the specific rows and columns of the table for faster access to the specific data. In the relational database model, data integrity is also a high concern, and this model uses several constraints to make sure that the data in the tables is accurate and reliable. While there are plenty of relational databases available, some of the most popular choices lately are:
- Oracle: Popularly known as Oracle RDBMS, it is an advanced multi-model DBMS that offers many advanced database features.
- MySQL is an open-source relational database management system that follows Structured Query Language for data manipulation. MySQL can run on all platforms like Windows, Linux, UNIX, etc.
- Microsoft SQL Server – This is another top RDBMS, which supports a range of transactional processing, analytics, and business intelligence applications.
- PostgreSQL – It is an object-relational DBMS offering extensibility and compliance with the standards.
- DB2 – It is an RDBMS system designed to store, retrieve, and analyze data efficiently.
Advantage of relational databases
- Relational DBs are highly matured and well-documented, and in use at many established enterprises.
- SQL follows highly standardized formats.
- There is a huge talent pool of qualified administrators and developers with SQL and RDBMS expertise.
- Relational databases are ACID-compliant, which means they can ensure Atomicity, Consistency, Isolation, and Durability.
Disadvantages of RDBMS
- RDBMS cannot work with semi- or unstructured data. So, they are ill-suited for many Big Data, IoT, and analytics workloads.
- Tables in the relational DBMS may not appropriately enable one-to-one mapping.
- Complex datasets with variable-length records are difficult to be handled with RDBMS.
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Non-relational or NoSQL databases
NoSQL databases are very popular alternatives to the conventional relational databases used largely by complex web applications. These can take various forms; however, the major difference between RDBMS and NoSQL DBs is that schemas of the former are very rigid, whereas NoSQL is schema-agnostic and can handle semi- and unstructured data well. Some of the DBs may fall into both these categories, i.e., Couchbase is both a key-value store and document-based DB.
Key-value stores under NoSQL like Amazon DynamoDB and Redis are very simple DBMS that can store only key-value pairs and offer some basic functionality to retrieve the values associated with known keys. Wide column store databases like Cassandra and Scylla etc., are also schema-agnostic, allowing the users to store data in columns and tables. In contrast, a single row can be considered as a record.
All these solutions are meant to meet the goal of scaling up and down to manage huge volumes of data across thousands of commodity servers in a distributed environment. Even though schema-free, the wide column stores use some SQL variants known as CQL for data manipulation, making it easier for those already familiar with SQL. Some document stores like Couchbase and MongoDB are schema-less and store data in JSON format. The document stores are also similar to the key-value and wide-column stores, in which the name of the document is the key, and the content of the documents are the values.
There are also graph databases under NoSQL DBs, representing the data as a network or related objects and nodes. For example, graph databases like Neo4j makes facilitates data visualization in a better manner. The graph database objects contain free-form data which is connected by relationships and grouped as per the labels. Graph database systems are designed to illustrate the connection between different data points.
More flexibility and schema-free database models are the major advantages of NoSQL databases. These are also horizontally scalable and more fault-tolerant compared to RDBMS. The data is effectively distributed across various nodes, which reduces the chances of a failover. However, as the disadvantages of NoSQL, these are not widely adopted in the IT support industry and matured as the conventional relational DBMS solutions.
