Is SQL Needed for Machine Learning?

Whether you are involved in machine learning as a data analyst, data scientist, or machine learning engineer, SQL is an important skill to have. The question is, how important is it—is it needed?

SQL is needed for machine learning. It is the de facto standard language for querying data; it is required to format data to be used by machine learning algorithms for improved pattern detection.

Of course, claiming that SQL is necessary for machine learning is one thing. Understanding why this is so is another. This article will present why SQL is needed in machine learning in an easy to understand format.

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The Link Between SQL and Machine Learning

The link between machine learning and SQL is data. Processing the amount of data required for machine learning requires proper querying. SQL is the language of choice to query data.

Many factors contribute to this popularity. SQL has been adopted as a standard by the American National Standards Institute (ANSI) and the International Organization for Standardization (ISO) organizations. Its syntax and command structure interact with data across different tables from multiple databases intuitively compared to programming languages.

To further appreciate the importance of SQL in machine learning, it is essential to understand the basic workflow that is involved in any machine learning project. You need to get from the raw data stage to the pattern detection stage. This process involves tapping into massive amounts of data. In most cases, the more data, the better the results will be.

SQL Is the Starting Point for Machine Learning

A query language such as SQL is required to manage and query such large amounts of data. This data can then be formatted in a way for machine learning algorithms to search for patterns. This pattern detection is what feeds machine learning.

In a way, at the most fundamental level, SQL allows data scientists and machine learning engineers to obtain the raw material for machine learning data. SQL knowledge can be analogous to the skill of drilling for oil. To operate machinery, you need fuel; oil is required to obtain refined fuel to run the machines.

In other words, without SQL knowledge, the machine learning process would seize.

How Does SQL Compare to Learning Python or R?

The need for SQL in machine learning does not diminish the role of other languages such as Python or R. Quite to the contrary, and it recognizes how different programming languages play an essential role in a machine learning workflow.

SQL Is More Imperative Than Python To Initiate Machine Learning

SQL rises above Python or R not because it is more powerful or more robust. Its importance comes from being the language used for “first contact” with the data needed for machine learning. SQL is the most straightforward language to query data. Additionally, in learning SQL, you can leverage efficiencies later by using SQL and Python in tandem.

Python is effective and efficient for writing applications, as well as developing and implementing algorithms. All of these are fundamental for machine learning. However, without the ability to query and manipulate the data, all of Python’s benefits in a machine learning context would not be possible.

Therefore, we return to the original premise of why SQL is needed for machine learning—you need SQL to properly query the raw data, which you can then use with other programming languages further down the machine learning workflow.

R’s Strengths Benefit From SQL

In machine learning, R shows its most significant benefit when it comes to statistical modeling. One can argue that it is unmatched in that regard. Additionally, R also provides an excellent way to create dashboards and other visualizations to monitor and evaluate the machine learning workflow.

That said, to get the most benefit out of R for machine learning, you need to integrate it into an SQL server. In other words, R can demonstrate its prowess in statistical modeling and visualization creation more effectively when it has access to a relational database. That is where SQL comes in.

R alone lacks an external storage engine. It relies entirely on its file system for storage. All of the operations conducted with R are performed in memory only. Doing so is what makes R impressively fast. However, this means that the data tables required to feed the statistical models it runs have to reside elsewhere.

For this reason, R has libraries of packages that allow you to connect to a SQL server. To use R in conjunction with a relational database for machine learning purposes, you need SQL.

Again, just as with Python, it is not that SQL provides you with a more extensive set of capabilities than R. Rather, SQL allows you to maximize R’s benefits.

Use SQL To Run Model Training Inside a Database

Another use for SQL is to run and build learning models for machine learning inside a database. In other words, conduct data queries, perform data analysis and run algorithms without performing a fetch function that has to pull data to an outside platform. Everything is done literally within the database.

When large data sets are involved, this can result in enormous performance efficiencies. It also allows for machine learning workflows to reside entirely on the cloud if so desired.

Examples of this would be running an Oracle database on the Oracle Cloud with machine learning functionality. Another example would be Google BigQuery ML.

In both instances, SQL can be used to run data queries as well as to build models. Using SQL in this manner, data analysts and data scientists can build and evaluate machine learning models faster.

The training models are kept on the same platform as the data. In doing so, it saves time in processing. Avoiding having to export data to an external data warehouse allows you to experiment more efficiently.

It opens up the possibility for data analysts—those who have a more intimate and intuitive feel for the data housed in the organization’s data warehouses—to participate more directly in the machine learning process. With only SQL knowledge, they can build and deploy models on their own.

These models can then be populated with data from the platform’s databases and used for model training and prediction.

It’s All About Parallelization

SQL knowledge in machine learning allows for parallelization. By this, we mean that it will enable you to combine the data access efficiencies provided by a language such as SQL with a parallel system’s performance and scalabilities, such as Python or R.

The result is building and deploying more models, finding more complex patterns, and making the machine learning workflow more efficient and the output more relevant.

Parallelization is made possible because SQL allows you to access common machine learning functions solely with SQL. More complex programming languages still play an important role, but their part is no longer exclusive.

In the process, this results in machine learning workflows being accessible to organizations with fewer human and computing resources. Thus, machine learning becomes more democratized within an organization as well as in general. In all of this, SQL is the price of entry.

Conclusion

In a machine learning workflow, data is king. The more relevant data you have, the better your modeling and pattern detection will be. As such, the effective querying of data from multiple sources is fundamental. SQL is the most effective language for these types of queries. For this reason, SQL is a must-have skill for machine learning.

Whether you approach machine learning as a data scientist or machine learning engineer, whether you incorporate other languages such as Python or R for your model deployment and statistical modeling, working knowledge of SQL is paramount.

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