SQL, or Structured Query Level, is a language that gives you access to databases. It allows you to manipulate databases as well. SQL is considered the future of data analytics for these and many other reasons.
But, people often wonder whether SQL is better than NoSQL in different industries. NoSQL stands for ‘not only SQL’ and stores more data than non-tabular databases. So, it’s natural to wonder why more companies are not opting for it.
Well, the answer to this isn’t as simple as you’d think. After all, SQL is the standard language for database management. But, this isn’t the only reason why companies are opting for SQL over NoSQL.
Let’s explore the reasons why SQL is beating NoSQL these days. This will also let us understand how this will affect data analytics in the future.
Differences between SQL and NoSQL
To understand why SQL is beating NoSQL, we must first understand their differences. There are a few areas in which these two languages differ. Let’s have a look at this one by one to understand how these differences affect data analytics.
We’ll discuss the two main areas in which SQL and NoSQL differ: structure and language.
SQL is mostly used by companies that prefer to have their data arranged in tables. These companies need SQL training for analysts to ensure their data is free from errors. So, it’s easy to see why SQL is ideal for applications needing multi-row transactions. Applications that need many rows usually reduce the response time for transactions.
Using these applications, you won’t have to enter many rows of data manually. SQL is the preferred language for these transactions. So, it’s no surprise that some of the biggest companies in the world use SQL.
These companies (like Accenture and Microsoft) process a large number of transactions. They need multi-row data apps to get through these transactions as soon as possible.
SQL is ideal for accounting and legacy systems that before used relational structures. Logical data structures are separate from the physical storage structures in relational data.
The separation enables administrators to manage physical data without accessing the logical structure. NoSQL databases are often in the form of key-value pairs and document-based. These databases have wide column stores and are also ideal for graph databases.
The NoSQL structure is better-suited to companies depending on large volumes of data. These companies don’t generally need relational databases. The NoSQL structure is perfect for unstructured data like social media and email.
So, it’s no surprise that companies like Google and Facebook use these kinds of databases. Mobile gaming companies prefer NoSQL databases.
One of the main differences between SQL and NoSQL databases is language. SQL databases use structured language for data manipulation. This also allows the database to take part in predictive data analytics.
SQL is pretty versatile which is why it’s widely used in several industries. But, the same features that make SQL versatile also make it restrictive. SQL would need you to use predefined schemas before working with your data structure.
This can leave it inflexible to changes should you need any. Also, remember that with SQL, all your data would need to follow the same structure. So, you would need to prepare well beforehand to execute data analytics properly.
But, NoSQL is more flexible and versatile with unstructured data. These databases have a dynamic schema which allows for data to be stored in more than one way. You can use graph-based and other kinds of data with NoSQL.
The advantage of using NoSQL here is that you don’t need to plan the structure of the data. You can keep adding fields to your data as you go along. So, it gives you more freedom to change data for predictive data analytics.
Despite this, companies are preferring SQL over NoSQL. Why? Let’s find out why SQL has been getting the upper hand lately.
Why SQL is Still a Popular Choice
SQL databases can process queries quickly by joining data across tables. This makes it easier for administrators to perform complex queries against structured data. This kind of structured data includes ad hoc requests.
But, NoSQL databases don’t have the same level of consistency across products. These databases need a greater amount of work to query data. So, it’s not simple to work with complex queries using NoSQL databases.
Over 200,000 companies are currently using Microsoft’s SQL server. This is a much higher figure than the tens of thousands of small businesses using NoSQL. Besides, a greater number of large companies use SQL rather than NoSQL.
Many IT teams continue to support SQL despite the introduction of cloud-based technology. They argue that SQL can be used in tandem with modern technologies to produce results. SQL is the better choice to protect data and ensure its integrity.
SQL and the Future of Data Analytics
SQL allows analysts to interact with data that is stored in relational databases. It allows analysts to access this data quickly. This is why it’s often preferred by companies that deal with quick customer response times.
Learning SQL is considered a critical skill for data analysts these days. This is because SQL is used for both data science and data analytics. This database structure allows you to spot patterns in large volumes of data.
In doing so, you’ll be able to gain deeper insights into the industry you’re working in. Also, it helps that SQL accommodates different kinds of data. These kinds of data include time series and geospatial data.
So, it’s easy to see why SQL is suited to various industries. The data-driven approach provided by SQL allows companies to make meaningful business decisions. Companies use data stored in SQL databases to forecast future business results.
Several companies use SQL to identify potential risks in their business initiatives. In this way, SQL plays a pivotal role in predictive analysis as well.
The future for SQL databases seems bright despite the introduction of modern alternatives. The traditional approach taken by SQL is found to be ideal for protecting data integrity. It also allows companies to reduce the response times on large transactions.
The biggest data-driven organizations around the world depend on SQL. This language makes it easy for them to analyze data from diverse sources. So, it ensures it remains a key player in the future of data analytics.