The five critical differences of SQL vs NoSQL:
- 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.
When it comes to choosing a database, one of the biggest decisions is picking a relational (SQL) or non-relational (NoSQL) data structure. While both are viable options, there are key differences between the two that users must keep in mind when making a decision.
Here, we break down the most important distinctions and discuss the best SQL and NoSQL database systems available.
The Big Picture Differences Between SQL and NoSQL
Think of a town - we’ll call it Town A - where everyone speaks the same language. All of the businesses are built around it, every form of communication uses it. In short, it’s the only way that the residents understand and interact with the world around them. Changing that language in one place would be confusing and disruptive for everyone.
Now, think of another town, Town B, where every home can speak a different language. Everyone interacts with the world differently, and there’s no “universal” understanding or set organization. If one home is different, it doesn’t affect anyone else at all.
This helps illustrate one of the fundamental differences between SQL (relational) and NoSQL (non-relational) databases, and this distinction has big implications. Let’s explain:
SQL databases: SQL databases use structured query language (SQL) for defining and manipulating data. On one hand, this is extremely powerful: SQL is one of the most versatile and widely-used options available, making it a safe choice and especially great for complex queries. On the other hand, it can be restrictive. SQL requires that you use predefined schemas to determine the structure of your data before you work with it. In addition, all of your data must follow the same structure. This can require significant up-front preparation, and, as with Town A, it can mean that a change in the structure would be both difficult and disruptive to your whole system.
NoSQL databases: NoSQL databases, on the other hand, have dynamic schemas for unstructured data, and data is stored in many ways: They can be column-oriented, document-oriented, graph-based or organized as a KeyValue store. This flexibility means that:
- You can create documents without having to first define their structure
- Each document can have its own unique structure
- The syntax can vary from database to database, and
- You can add fields as you go.
In most situations, 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. NoSQL databases, on the other hand, are horizontally scalable. This means that you handle more traffic by sharding, or adding more servers in your NoSQL database. It’s like adding more floors to the same building versus adding more buildings to the neighborhood. The latter can ultimately become larger and more powerful, making NoSQL databases the preferred choice for large or ever-changing data sets.
SQL databases are table-based, while NoSQL databases are either document-based, key-value pairs, graph databases or wide-column stores. This makes relational SQL databases a better option for applications that require multi-row transactions - such as an accounting system - or for legacy systems that were built for a relational structure.
Related Reading: PostgreSQL vs MySql - Which is Better?
The Best SQL Database Systems
Now that we’ve established the key structural differences between SQL and NoSQL databases, let’s delve a little deeper into this topic by reviewing the best SQL and NoSQL database options available right now.
We'll start with SQL database systems. Keep in mind that the best SQL database systems now offer compatibility with NoSQL. Nevertheless, they still work best with relational SQL structures.
Here are some MySQL benefits and strengths:
- Owned by Oracle: Although MySQL is free and open-source, the database system is owned and managed by Oracle.
- Maturity: MySQL is an extremely established database, meaning that there’s a huge community, extensive testing and quite a bit of stability.
- Compatibility: MySQL is available for all major platforms, including Linux, Windows, Mac, BSD, and Solaris. It also has connectors to languages like Node.js, Ruby, C#, C++, Java, Perl, Python, and PHP, meaning that it’s not limited to SQL query language.
- Cost-effective: The database is open-source and free.
- Replicable: The MySQL database can be replicated across multiple nodes, meaning that the workload can be reduced and the scalability and availability of the application can be increased.
- Sharding: While sharding cannot be done on most SQL databases, it can be done on MySQL servers. This is both cost-effective and good for business.
- Who Should Use It? MySQL is a strong choice for any business that will benefit from its pre-defined structure and set schemas. For example, applications that require multi-row transactions - like accounting systems or systems that monitor inventory - or that run on legacy systems will thrive with the MySQL structure.
Another popular SQL database system, particularly with enterprise-level organizations, is Oracle Database. Oracle Database offers the following strengths and benefits:
- Professionally developed and managed: Oracle develops and manages the Oracle Database system. As a commercial option, this relational database management system benefits from frequent updates and excellent customer support.
- A Unique SQL "dialect": Oracle Database uses its own dialect of SQL known as PL/SQL (Procedural Language/SQL). This language differs in small ways from traditional SQL, primarily in how it deals with stored procedures, built-in functions, and variables.
- Expensive: As a professionally developed and managed database system, Oracle is one of the most expensive options available.
- Compatibility: Oracle Database is available for any operating system.
- DBMS Organization: Oracle groups its objects by schemas that are a subset of database objects.
- Large database sizes: Oracle can handle extremely large databases, making it an excellent choice for enterprise companies with large data needs.
- Easy to upgrade: With Oracle Database, you can complete an upgrade without a needing to overhaul the system completely.
- Transaction control: With Oracle, new database connections are new transactions. You can make rollbacks and changes because values won't change prior to commit.
- Other benefits: Oracle offers bitmap indexing, partitioning, function-based indexing, reverse-key indexing, and star query optimization.
- Who Should Use It? Oracle Database is an excellent database choice, but the costs could prevent small-to-medium-sized organizations from taking advantage of it. For an enterprise organization that has large data needs and a generous budget, this solution could be a match.
Microsoft SQL Server
Microsoft SQL Server is a popular option for small-to-medium-sized companies. It offers the following benefits and advantages:
- Professionally developed and managed: Microsoft develops and manages the Microsoft SQL Server database system. As a commercial relational database management system, customers benefit from frequent updates and great user support.
- A Unique SQL "Dialect": SQL Server employs its own dialect of SQL, called T-SQL (Transact SQL). Like Oracle, this differs from traditional SQL in how it handles built-in functions, stored procedures, and variables.
- Compatibility: SQL Server only works with Windows and Linux based systems.
- Transaction control: Since SQL Server has a separate execution of each command, it's hard to make adjustments mid-process when errors are found.
- DBMS Organization: SQL Server organizes tables, procedures, and views according to database names.
- Easy to use: SQL Server has a reputation for being easy to use. According to this reviewer: "The interface is easy to understand, the error-checking is strong (and it actually tells you what is wrong)."
- Excellent support: As a Microsoft product, SQL Server includes live product support, and excellent documentation.
- Other features: SQL Server features some great tools and features like BI tools, Database Tuning Advisor, SQL Server Management Studio, and SQL Server Profiler.
- Who Should Use It? Microsoft SQL Server is an excellent choice for small-to-medium-sized organizations that need a high-quality, professionally-managed database system with excellent support, but don't require the cost or scalability of an enterprise solution like Oracle.
We listed PostgreSQL last among the SQL DBMS's because it's a hybrid SQL/NoSQL database system that finds a middle-ground between these two options. PostgreSQL offers the following strengths and benefits:
- Cost-effectiveness: PostgreSQL is a free and open-source database system. The PostgreSQL Global Development Group develops and manages the system.
- ORDBMS: PostgreSQL is an "Object Oriented Database Management System" (ORDBMS), not simply a "Relational Database Management System (RDBMS). This means it serves as a hybrid between a strictly relational model (SQL) and a strictly object-oriented model (NoSQL).
- User support: PostgreSQL doesn't have its own customer support, per se, but there is an active community that will readily provide free support. Moreover, excellent paid support options are available from third-party service providers.
- High ACID Compliance: PostgreSQL is known for offering the highest levels of atomicity, consistency, isolation, and durability. These are the four standards experts use to judge the quality of a database design. Learn more about ACID compliance here.
- Pure SQL: Another benefit of PostgreSQL is the fact that it utilizes one of the purest forms of SQL available, as opposed to other database systems that often have unique variances.
- Who Should Use It? As a hybrid between a relational database and an object-oriented database, PostgreSQL is excellent when your data doesn't mesh well with a perfectly relational model. It works great for extra-large databases and for performing complicated queries.
NoSQL Non-Relational Database Systems
Now, let's move onto the various NoSQL non-relational database systems. These systems require a little more technical expertise to understand. We'll start with MongoDB.
The following are some of the benefits and strengths of MongoDB:
- Free to use: Since October 2018, MongoDB's updates have been published under the Server Side Public License (SSPL) v1, and the database is free to use.
- Dynamic schema: As mentioned, this gives you the flexibility to change your data schema without modifying any of your existing data.
- Scalability: MongoDB is horizontally scalable, which helps reduce the workload and scale your business with ease.
- Manageability: The database doesn’t require a database administrator. Since it is fairly user-friendly in this way, it can be used by both developers and administrators.
- Speed: It’s high-performing for simple queries.
- Flexibility: You can add new columns or fields on MongoDB without affecting existing rows or application performance.
- Not Acid Compliant: As a NoSQL database, MongoDB is not ACID compliant. See PostgreSQL above for more about ACID compliance.
- MongoDB Atlas (a new feature): MongoDB recently added MongoDB Atlas global cloud database technology to its offerings. This feature allows you to deploy fully-managed MongoDB via AWS, Azure, or GCP. MongoDB Atlas lets you use drivers, integrations, and tools to reduce the time required to manage your database. Here's the pricing information from Atlas.
- Who Should Use It? MongoDB is a good choice for businesses that have rapid growth or databases with no clear schema definitions (i.e., you have a lot of unstructured data). If you cannot define a schema for your database, 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, content management systems, etc. - MongoDB can be a strong choice for you.
Apache Cassandra (or Cassandra DB) was originally a Facebook product, but in 2008, Facebook released it to the world as a free, open-source NoSQL database system. Here are some of Cassandra's benefits and strengths:
- Free and Open-Source: After Facebook made Cassandra open-source, Apache took over the project in 2010.
- 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.
- Active everywhere: Users can write and read from all Cassandra nodes.
- Fast writes and reads: Cassandra's design speeds up read and write commands tremendously via its distributed, highly-available organization, even in the case of massive projects.
- Not ACID Compliant: As a NoSQL database, Cassandra is not ACID compliant, instead Cassandra offers atomic, isolated and durable transactions with eventual consistency.
- Support for SQL: Even though it's not ACID compliant, Cassandra does offer some support for SQL via SQL-like DDL, DML, and SELECT statements.
- Poor with updating and deleting data: Cassandra is not optimized for updating and deleting data.
- Offers excellent data protection: Cassandra features a commit log design that makes sure data isn't lost. It also features backup/restore which adds additional data protection.
- Redundancy of data and node function: Cassandra offers constant uptime and eliminates singular points of failure.
- Who Should Use It? Cassandra is most popular for use with IoT (internet of things) technology because it offers fast, real-time insights. It 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 Cloud BigTable
- Low latency: According to Google, BigTable offers a consistent sub-10ms latency.
- Replication: Through replication, BigTable provides higher availability, durability, and resilience when zonal failures happen. Replication also offers "high availability for live serving apps, and workload isolation for serving vs. analytics."
- Machine learning: BigTable features a storage engine for use with machine learning applications.
- Easy to integrate: Integrates well with open-source data analytics tools.
- Highly scalable: Google BigTable can work with massive data sources in the hundreds of petabytes scale.
- Fully managed with Integrations: Like MongoDB Atlas, BigTable is fully managed, which reduces workload requirements. It also integrates instantly with many platforms, which streamlines the ETL processes required to load data.
- Highly compatible with Google services: As a Google product, BigTable integrates well with other services under the Google umbrella.
- When Should You Use It? According to Google, BigQuery is great for fintech, IoT, and advertising technology as well as other use cases. For fintech, you can create a check for fraud patterns and watch real-time transaction information. You can also save and consolidate financial market data, trading activity, and more. For IoT, you can ingest and understand massive amounts of real-time time series data recorded from sensors to create dashboards and valuable analytics. For advertising, you can gather large amounts of customer behavior data to find patterns that inform your marketing efforts.
As a database modeled after Google BigQuery, Apache Hbase was created to work with large datasets. Here are some of the benefits and strengths of HBase:
- Open-source and free: Apache HBase is an open-source, free, NoSQL database system managed by Apache. It was modeled after Google Cloud BigTable (above), to offer BigTable-like features on top of the Hadoop Distributed File System (HDFS).
- Massive tables: HBase was specifically created to manage large datasets.
- Scales across a cluster: Hbase is excellent at scaling across a cluster. Clusters relate to clustering algorithms, which are used to derive machine learning insights from data.
- Data management: 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: As a NoSQL database, HBase is ideal for storing both semi-structured and structured information.
- Consistency: HBase offers fast, consistent processing of read and write commands. After performing a write, all of the read requests on the data will produce the same response.
- Failover: HBase uses replication to offer failover, which reduces or eliminates the negative impact of a system failure on users.
- Sharding: HBase offers automatic and configurable sharding for tables.
- When Should You Use It? 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.
Migrate Data Into Your Database the Easy Way
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