You may be happy with your current database solution, but are you sure you’ll feel the same way in the next five years? What about the next two? Data grows at a constant level, which has serious ramifications on applications and the choice of database to store and consume this data.
In this article, we compare two popular types of databases: NoSQL vs. SQL database. We’ll look at the differences in terms of structure, rigidity, use cases, and popularity. We also look at how NetApp Cloud Volumes ONTAP can enhance the use of your databases running in the cloud.
What Is SQL Database?
Relational databases, also known as SQL databases, are based on the Structured Query Language (SQL), where data is stored according to a defined structure in tables comprising rows and columns. Each row in an SQL database represents an entry and each column represents a field like Name, Department, ID and so on.
SQL, which was originally developed at IBM in the early 1970s, is a declarative programming language that is mainly used for operations such as data query, data manipulation, data definition, and data access control.
SQL databases adhere to a predefined structure called schema, which defines database elements including the relationship between two or more tables, views, indexes, procedures, types, and functions. This well-defined schema helps ensure that the data in SQL databases is not redundant and that it is not out-of-sync. Although it is important to have a well-defined schema, making sure it is not so rigid is important too: you don’t want a small change like a change in data type to wind up editing the whole database. SQL databases can scale vertically which usually means scaling the compute and storage of the DB instance.
The declarative language of SQL can be a real gift for developers. The JOIN clause, for example, allows combinations of rows from two or more tables based on related columns. This allows data retrieval in simple SQL syntax instead of having to write separate code for them.
SQL databases use the ACID database properties to ensure that the database transactions are reliable. ACID stands for Atomicity (An “all or nothing” approach for the data that is committed to be saved), Consistency (Interrupted changes are rolled back), Isolation (Intermediate state of a transaction is not visible to other transactions), and Durability (Completed transactions retain their state even in system failure).
Owing its database integrity to the ACID properties, SQL databases have found widespread adoption in complex transactional applications with use cases ranging from big data analytics to financial institutions. One of the biggest advantages of the SQL platform is its maturity and support, including from third-party and open-source vendors.
Take MySQL for example. MySQL, which is an open-source SQL platform, is one of the most popular databases in the world, with customers ranging from social media heavyweights like Twitter and Facebook to financial services like JP Morgan and Bank of America. It provides several support options under the Oracle umbrella and is a major component of the LAMP stack making it very popular among developers. Some other popular SQL databases include MSSQL, Oracle, IBM DB2, Sybase, MariaDB, and PostgreSQL.
What Is NoSQL Database?
NoSQL databases, also referred to as “non-SQL” and “Not Only SQL” databases, are mainly used for unstructured data. Data is not stored in tabular format but is stored mainly in documents, key-value pairs, graphs, or wide column stores format. As NoSQL databases are schema agnostic, unstructured data such as blog articles, photos, videos, or other content can be stored very easily.
There are a few factors when it comes to NoSQL vs. SQL performance. The agnostic schema approach also results in agile software development as there is no rigid predefined schema structure to adhere to. The downside is that, as data is not organized as it is in SQL, NoSQL requires more specialized processing tools like Apache Hadoop to process the data in a readable format. It also has high storage requirements.
Due to its distributive nature, NoSQL databases follow the Brewers CAP Theorem, which states that it is impossible to simultaneously provide more than two out of the three guarantees: Consistency, Availability, and Partition tolerance. Consistency states that the last successful write is returned every time, ensuring every client has the same view of the data. Availability ensures that every request receives a non-error response in a reasonable amount of time. Partition tolerance dictates that the system must still honor its consistency guarantees, keeping the network partitions in mind.
Unlike SQL databases, NoSQL databases are more horizontally scalable which means that the load can be distributed by adding more database servers in the pool. This elastic scalability is a big advantage in providing optimal support for the MapReduce programming model, which makes NoSQL the perfect candidate for big-data applications. Some of the most common NoSQL databases are MongoDB, MarkLogic, CloudDB, and Dynamo DB.
Although NoSQL databases are seeing a rising popularity in mainstream applications, its community might not be as popular and extensive as an SQL community like MySQL. Its lack of standardization and reporting tools has also proved to be a major disadvantage.
Which Is Right for You?
So when it comes to the NoSQL vs. SQL database comparison, deciding which one is best for you will most likely depend on what you want to use your database for and your application’s requirements. Let’s take a look at using SQL and when to use NoSQL vs. SQL.
You would choose an SQL database when:
- You need a database with a predefined schema so that applications adhere to that schema.
- You are designing an application that requires multi-row transactions.
- You require a database that has no room for error and is very consistent, for example in the case of data warehousing systems.
When to use NoSQL databases:
- You need a database that accounts for exponential growth with no clear schema definitions.
- You require a database which can accommodate variable data structures and plays well with big data platforms such as Hadoop.
- You need a distributed database system that scales easily and inexpensively.
Running SQL and NoSQL Databases in the Cloud
Now that we’ve looked at the SQL vs. NOSQL pros and cons, another consideration to factor in is running your SQL or NoSQL database in the cloud. As databases grow exponentially in size, so do the resources required to effectively host them and service clients. One of the biggest advantages of running databases in the cloud is the quick turnkey enterprise features like availability, scalability, disaster recovery, and durability, not to mention the very low OPEX and no additional CAPEX.
Another major advantage is the use of managed database services or DBaaS, both in the SQL and the NoSQL fields. These DBaaS services ensure that daily maintenance tasks such as infrastructure provisioning, database setup, patching, and backups are automated, freeing up developers to deploy DBs at scale without worrying about managing the underlying infrastructure. That goes a big way to improving the software development lifecycle. Amazon RDS and Azure Databases are two popular examples of DBaaS services. To find out more about databases in the cloud, see this article.
Databases with NetApp Cloud Volumes ONTAP
When hosting your own databases in the cloud, users need to consider factors such as high performance and throughput, reliability, data security, and keeping down costs. This is where NetApp Cloud Volumes ONTAP shines.
Cloud Volumes ONTAP for AWS storage or Azure storage provides the highest possible levels of data protection and availability for databases, which in turn translates to zero RPO and minimal RTO. Cloud Volumes ONTAP provides an HA configuration where two different Cloud Volumes ONTAP nodes can be hosted in different AWS Availability Zones or Azure Availability Sets.
NetApp Cloud Volumes ONTAP’s storage efficiency features such as data compression and deduplication can produce significant cost savings, especially as data change rates in databases can increase rapidly. The data tiering feature provides further cost savings by using object storage as a capacity tier. This can be a big help in avoiding DBaaS storage limits.
When it comes to data protection, Cloud Volumes ONTAP utilizes NetApp Snapshot™ technology, which allows nearly instantaneous, application-aware snapshots and significant time savings compared to the native snapshot technologies. These snapshots can also be restored instantly using the SnapRestore® technology and automatically managed via the SnapCenter® GUI.
For more on databases, take a look at our series on cloud-based database, which covered the challenges of cloud-based DBs, SQL, Oracle, and NoSQL.