As the world begins to generate more data than ever before, a range of tools has been created that help to deal directly with the rising tide. The market for products and tools that help us deal with data is on a rising trajectory, predicted to reach $103 billion by 2027. One of the leading methods we use to interact with and draw analysis from data we collect in business is SQL.
SQL, which stands for Structured Query Language, is one of the leading languages for analyzing databases and finding relationships within them. Currently, it is the standard language that’s used whenever a database needs a set of management tools, with this being a standard used across the United States.
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Companies including Microsoft, Oracle, and other tech giants regularly use SQL for analysis because of its effectiveness. What’s more, as completely open-source software that can be found and implemented easily, SQL is both inexpensive and efficient, making this the best possible option for businesses, regardless of the size of their operations.
One common myth about SQL is that it doesn’t work at scale, with some people citing that it cannot deal with a certain level of performance. While it’s certainly more difficult to manage databases with SQL at scale, this is simply because processing and organizing large quantities of data is, of course, harder than doing so with smaller amounts of data.
In this article, we’ll be delving into the world of SQL for database analysis, demonstrating exactly when you should be using this language for managing data.
Use SQL if Your Data Structure Is Relational
When your data is in highly structured and regulated formats, like many strictly formatted tables across lots of different areas, then SQL is the perfect language for its analysis. Quite literally including structure in its acronym, this is a central part of what makes this language so effective.
Suppose you have highly structured data and need to draw relationship connections across those specific tables. In that case, SQL will be your go-to language, as it works perfectly for any strictly formatted structure. Alternatively, if your data is in many formats or structures, then SQL isn’t the best language for you.
Use SQL if You Want Rapid Queries
Following on from the specific structure that SQL thrives with, this language is fantastic if you need to make a lot of queries quickly. If you’re a business that needs to comb through many datasets, join them, and run queries on them, then this is the perfect language in which to create your database.
For example, if you needed to run complex queries, adding in a range of additional functions throughout, then SQL would be able to do so rapidly and without too much strain on the system. The ability to rapidly query structured data is why this is the preferred structural language for most developers.
Use SQL if You Need a High Degree of Data Integrity
Data integrity is the general accuracy and consistency of all the data that a company stores, either in an overarching data warehouse or simply within a database they’re analyzing.
SQL is a great option whenever a company needs a high degree of data integrity as SQL requires data to align with ACID.
ACID, standing for atomic, consistent, isolation, and durability, are rules that SQL sets out for their databases. SQL imposes these rules to ensure that any databases run with SQL are consistent and robust, helping queries to be made quickly and efficiently.
Use SQL if You Want to Scale Vertically
When you build a data warehouse with SQL, you are fairly limited in the direction in which you can scale. With other languages, like NoSQL, you can scale horizontally, accommodating many different data stores. However, with SQL, the only way that you can scale your databases is vertically.
Vertical scaling means that it’s fairly easy to add more resources, like memory or CPU storage, onto your database. This is fantastic for increasing the efficiency of your queries and should be favored by any businesses that need to access query results as quickly as possible.
That said, if you’re using a cloud data warehouse, the ability to scale at all is tied to the provider you use. For example, if you look at a comparison between Druid vs. Clickhouse, you can see that the former allows for new nodes, while the latter requires a much more manual process that could pose a problem.
Be sure to check with your cloud data warehouse provider before considering this as the main reason to opt for SQL.
When Not to Use SQL
While SQL is, as demonstrated above, a very useful language for database analysis and changing data, it is not the only way of managing data. Many businesses will use a NoSQL database when they come across unstructured data.
SQL is very efficient at managing structured information, providing an easy way to launch queries and get results in a few seconds. However, much of the data companies will collect is unstructured, coming into the company’s data pipeline in various formats.
If your business is actively dealing with many different formats, then you may be better off using NoSQL. This language is less commonly used and often requires a data scientist to run queries properly due to the extra processing steps that data will need. However, if your database needs to grow horizontally, then NoSQL will be the better choice.
In most cases, SQL will be the language of choice for querying data in a database, given that it’s extremely versatile and can process data quickly. However, that doesn’t mean SQL is the only language that is used in the world of data science, with NoSQL databases being fairly common.
Depending on the structure of data that you’re working with, as well as the future scaling that you’re planning on doing, structuring your database with NoSQL may be the better option. Remember to always consult a data engineer when constructing your data warehouses, as they will help guide you to the right configuration for your business.