Applications of SQL in AI

The major applications of SQL include writing data integration scripts, setting and running analytical queries, retrieving subsets of information within a database for analytics applications and transaction processing, and adding, updating, and deleting rows and columns of data in a database.

The world today generates around two and a half quintillion bytes of data each day, which, according to analysts, will reach four hundred and sixty-three exabytes by 2025, propelled by the increasing popularity of the Internet of Things (IoT).

Companies of all sizes are jockeying for a share of the massive amount of data generated via internet-connected hardware, striving to store the information in their databases for actionable insights that drive business growth.

Organizations, however, need a domain-specific, standardized solution to manipulate, process, and access information stored in their databases, and this is where SQL, or Structured Query Language, comes into play.

What Is SQL?

Developed by Donald D. Chamberlin and Raymond F. Boyce in the early 1970s, SQL, even to this day, remains the most preferred query language for streamlining the management of database structures.

Compatible with most modern technologies, SQL simplifies the data collection, storage, and information retrieval process while allowing a database to run on multiple computers concurrently, thereby heightening its usability in workplaces.

Top companies, such as Netflix, Uber, Instagram, Flipkart, Amazon, and Linkedin, as well as many small businesses, leverage the Structured Query Language to extract information for data-centric analysis, database upgrades, and maintenance.

Deep knowledge of SQL is an asset for anyone who wants to secure a data-related job. Let’s talk about ten practical applications of SQL that you need to be acquainted with to understand how SQL expands job opportunities.

Applications of SQL in Healthcare

Organizations in the healthcare industry typically make use of SQL to analyze large data sets containing information about patient behavior, their medical conditions, and demographics. Healthcare data analysis allows medical professionals to gain critical insight, which contributes toward developing specific solutions for solving problems.

Use of SQL in the Finance Industry

Apps from banks, financial institutions, and online payment processors, for example, Stripe, store vital data about users and their financial transactions. Behind the scenes are complicated database systems powered by SQL, which not only enable the delivery of personalized experiences to users but also enable the extraction of actionable insights that serve to check for fraud.

Applications of SQL in Social Media

Most social media networks process a significant amount of data every second. Platforms, such as Snapchat and Instagram, make use of SQL to store the profile information of users. The Structured Query Language allows them to update the database of their app when users create new posts or share photos, and it also facilitates the recording of messages, enabling users to retrieve messages later.

Use of SQL in Music Apps

SQL helps music apps, such as Pandora and Spotify to store and process data related to user preferences, allowing these apps to make personalized recommendations from vast libraries of music albums and music files by different artists.

Applications of SQL in Marketing

In addition to the obvious applications of SQL in data administration and data science, the query language is also being increasingly used in strategizing marketing policies. Companies, both tech and non-tech alike, are relying on SQL to identify their target audience, learn more about consumer behavior patterns, and analyze the effectiveness of running marketing campaigns.

Use of SQL in Back-end Development

Back-end web developers are responsible for facilitating the integration of databases with front-end software, minimizing data corruption, and eliminating bugs – all of which ensure the seamless delivery of applications to end-users. The use of SQL is widespread in back-end web development, as it allows for an upgrade, insertion, and retrieval of data.

Applications of SQL in Database Administration

The primary task of a database administrator is to update an organization’s online database and maintain its integrity, so that data can rest in a secure environment. Database administrators of businesses, universities, hospitals and other such institutions make use of SQL to capture and process confidential information about users, employees, students, or patients, without much hassle.

Use of SQL in Data Analysis

The responsibility of a data analyst involves the sorting of massive data sets and discovering trends and combinations from data that organizations can use to efficiently manage their business. The use of SQL plays a crucial role in data analysis, as the Structured Query Language can streamline the process of gaining insights from huge volumes of data, utilizing a variety of conditional commands.

Applications of SQL in Structuring the Architecture

To support the server/client architecture, software engineers rely on SQL to establish the connection between the back-end and front-end. Utilizing the query language as part of 3-tier architecture involving the client, database, and application is also a key use of SQL that provides a logical structure to the design architecture.

Use of SQL in Data Science

In data science, the use of code and algorithms in SQL is wide-ranging. Data scientists make use of SQL code and algorithms to create a data model that analysts can use to explore data and discover business-specific trends and combinations in that data.

Benefits of Using SQL

The use of SQL brings a wide range of advantages, which include:

  • Faster Processing Speed: SQL offers high-speed data processing that allows for faster data retrieval.
  • Minimal Requirement of Coding: SQL’s code structure is simple and user-friendly. Even people with no experience in coding can learn SQL.
  • Easier Data Manipulation: With a few queries, data analysts and scientists can view, analyze, or update the data in a database.
  • Better Data Mining: The Structured Query Language sorts and filters data efficiently, making data more useful and relevant.
  • Restricted Access: Password-protected systems ensure a secure environment.
  • Reliable for Complicated Queries: In comparison to other DBMS or Database Management systems, SQL is more reliable for complex queries.

Must-Have Skills for a Career in SQL

Skills in Structured Query Language help data professionals create, maintain, and retrieve data from a relational database while allowing for efficient updating, manipulation, and insertion of data. Essential SQL skills to jumpstart a career as an SQL practitioner include:

  • Familiarity with Microsoft’s SQL server
  • Database management expertise
  • Knowledge of PHP
  • Proficient with Indexing
  • Mastery over related SQL systems, such as PostgreSQL and MySQL
  • OLAP (Online Analytical Processing) skills
  • Thorough understanding of SQL Joins
  • Acquaintance with database architecture

Doing Data Science and AI with SQL Server

Data is an important asset for every business. Whether you’re in the retail, health care, telecommunications, utilities or financial businesses, you’re familiar with the following two use cases:

  • In an online transaction processing (OLTP) scenario, transactional data is stored in a database. The transaction data is produced by various line-of-business (LOB) applications.
  • In a data warehousing scenario, data is acquired from various heterogeneous data sources, transformed and cleansed, and loaded into data warehouses. The consolidated data provides the single source of truth for business reporting and dashboards. At the same time, it also enables interactive analysis via multi-dimensional online analytical processing (OLAP) cubes, and tabular models.

Getting from raw data to insights empowers business decision makers to gain a deeper understanding into each aspect of the business and helps them react to new business situations quickly. For example, consider a retail scenario. The business analyst notices that sales are dropping for specific retail stores. The business analyst wants to drill down to understand the details on what’s causing the drop in sales. By being able to run the analysis (aggregating, joining of data from multiple data sources, filtering and so on) on a large amount of data, it enables deep analysis of customer behavior and trends in the retail stores. Microsoft SQL Server powers these mission-critical applications.

Many companies have started on digital transformation to mod­ernize their data platform to keep pace with the ever-growing requirements on the type of data that needs to be stored and the volume in which the data is being acquired.

As part of this digital transformation, advanced analytics plays an important role. Specifically, companies have been either building up data science teams within their companies or leveraging external resources to do data science. They use data science to distill data assets into nuggets of gold that can help them proactively deliver personalized customer experiences (personalized Web sites, product recommendations, customer lifetime value and so on), reduce downtime for equipment (predicting remaining useful lifetime) and more. The potential use of data science and how it can literally change businesses is exciting.

Some common use cases (non-exhaustive) of data science include the following:

Identifying Loan Credit Risk: A lending institution (a credit bureau) might want to leverage loan credit risk models to determine the borrowers that are likely to default and reduce the number of loans given to these high-risk borrowers.

Managing Customer Churn: Customer churn models have been used extensively (in retail and by telecommunication providers). For example, customers leveraging mobile services offered by telecommunication providers have a rich variety of choices and can easily switch between service providers. Managing customer churn is important to reduce customer acquisition costs and maintain a high-quality service. In addition, retail companies are using churn models to predict customers that are most likely to churn and to understand the key factors that lead to those churns.

Reducing Hospital Readmission: Reducing readmission rates for various medical conditions (heart attack, pneumonia, coronary artery bypass surgery, to name a few) is important to hospitals. In the United States, hospitals face penalties if the readmission rate is too high. Hospitals leverage predictive models for predicting patients that are more susceptible to being readmitted within 30 days. This helps them understand the root causes for the 30-day readmission, and helps them work toward addressing them.

This presents an exciting opportunity for database professionals and developers to either work with data scientists, or put on a data scientist hat to build predictive models that can help to assess credit loan risk, manage customer churn, reduce hospital admissions and more. The possibilities for developers to turn all these raw data assets sitting in the database to golden, strategic insights is exciting.

This article shows how you can work with data science and artificial intelligence (AI) with SQL Server. You’ll also learn how to jump-start your journey of using R and Python with SQL Server.

Why Doing Data Science with SQL Server Matters

What does doing data science mean and why does it matter to the database person? Today, most data scientists first figure out how to connect to many data sources (databases included), bring the data out from the database, and use the historical data to train and subsequently test the machine learning models that they’ve built.

A typical approach used by data scientists is to read the data from the database into the client that they’re using for building the model. Once the data is read, they combine the data with other data sources. For data scientists developing the models using R, packages like dplyr are commonly used for performing aggregation, joins and for filtering. With the data transformed into the right shape, data scientists continue the data modeling process, and start doing feature engineering. As part of feature engineering, new features (such as adding new columns in a SQL Server table) might get created, existing features might get transformed (scaling it to -1 to 1, or 0 to 1, applying logarithmic transformation, computing the z-score, binnning the data, and so on) or removed. Feature engineering plays a very important role in laying the groundwork needed for a good predictive model. Once all these steps are completed, the data scientist develops the models and validates it using test data before figuring out an operationalization plan for the model to be deployed to production so that applications can consume them.

At this point, as a database person, you might ask, “Why do I need to move the data out from the database to do data science? Should we push the processing of joins and aggregations (Sum, Min, Max and so on) into the database?”

Why does it make sense to do this in the database? First, data movement is expensive. If the machine learning models can run where the data is stored, this removes the need to move data between the database and the client application. Second, a new working copy of the data is extracted from the database and stored external to the database. The implication is that many of the security policies and audits that apply to data stored in the database can no longer be enforced. Third, if the computation of joins and aggregations can be done where the data is located, you can leverage decades of database innovations (leveraging indexes—clustered and non-clustered, in-memory tables, column stores, high-availability and so on). If training the model can be done where the data is stored, it can lead to performance improvements.

In addition, if the data science project involves working with spatial data, temporal data or semi-structured data, you can leverage SQL Server capabilities that let you do this efficiently. For example, if you’re working on a data science project (say a land-use classification problem) where you must manipulate spatial data, the geography and geometry data types in SQL Server will provide a good way to store the spatial data. Once it’s stored as spatial data in SQL Server, you can leverage SQL Server spatial functions to query for nearest neighbors, compute the spatial distance using different spatial reference systems and more. The spatial data can be indexed to facilitate efficient query processing.

As a database professional and developer, you have tremendous knowledge and value to bring to a data science project. By doing data science and AI where the data resides, there are many benefits. These include being able to take advantage of the enterprise-grade performance, scale, security and reliability that you’ve come to expect from SQL Server over the years. More important, you eliminate the need for expensive data movement.

Another important consideration in data science projects is operationalization. The predictive model that has been developed by data scientists needs to be deployed into a production environment for it to be used by applications. With the release of SQL Server 2016 R Services, you can wrap R code as part of the stored procedures. After training is done, the predictive models are stored as varbinary(max) in a database table.

An application (.NET, Java, Node.js and more) would connect to SQL Server and invoke the stored procedures to use the predictive model for making predictions on new instances of data. Continuing the momentum, SQL Server 2017 CTP2 added Python support. You now have the best of multiple worlds: the ability to write code in R or Python, leverage the rich set of R and Python libraries for machine learning and deep learning, and consume the predictive models in any application.

Using Stored Procedures for Machine Learning and AI

By encapsulating the machine learning and AI models as part of the SQL Server stored procedure, it lets SQL Server serve AI with the data. There are other advantages for using stored procedures for operationalizing machine learning and AI (ML/AI). These include:

Applications can leverage existing database drivers to connect to SQL Server: Many programming languages have database drivers that enable them to connect to SQL Server. These database drivers (OLEDB, ODBC, JDBC, MSPHPSQL and Node.js Driver for SQL Server) are used by application developers to develop cutting-edge applications that “talk” to SQL Server.

In addition, companies might have existing LOB applications that are already operational. By leveraging ML/AI-stored procedures in SQL Server, these LOB applications can easily evolve into intelligent applications. With the R or Python code needed to work with the ML/AI models encapsulated in stored procedures, application developers can now leverage their ML/AI stored procedures as is (without requiring new libraries or learning new database access patterns). To the application layer, the intelligent ML/AI stored procedure behaves just like any SQL Server stored procedure.

Being backed by the full spectrum of SQL Server enterprise-ready capabilities: Some considerations include …

  • Where can I host the model for doing inference?
  • Which users can access the ML/AI model?
  • When the model is used for prediction (aka “inference”), it might need to access specific data in the database. In what security context should the model and associated R/Python code execute?
  • How can I ensure the R/Python code doesn’t use up all the SQL Server resources?

SQL Server provides enterprise-ready capabilities from using row-level security to limit the data that can be accessed, to providing database admins with both server and database scoped database audits, to enabling ownership-­chaining for SQL Server securable, to being able to sign stored procedures with a certificate or asymmetric key, resource governance and more. These enterprise-ready SQL Server capabilities can be used by the ML/AI stored procedures as is, without requiring the data scientist to reinvent the wheel for serving data at scale. Most important, the DBAs today can leverage their existing skills to secure and manage the ML/AI stored procedures.

Mature development tools to develop the ML/AI stored procedure: As a database developer, you can develop the stored procedure and the R and Python code in one place: Visual Studio. With the availability of SQL Server Data Tools for Visual Studio, R Tools for Visual Studio, and Python Tools for Visual Studio, you can do development of the T-SQL, R, or Python code, check it into a source control system, write unit tests, automate testing, and perform code review, and more. Database developers and data scientists can now work together to develop the ML/AI stored procedures, each focusing on their respective areas of expertise.

Steps to Get Started with SQL Server, Data Science and AI

There has never been a more exciting time and better opportunity for us as database professionals and developers to work with data science and AI with SQL Server. You can get started in three easy steps:

Install SQL Server 2016 or SQL Server 2017 CTP2. When installing SQL Server 2017 CTP2, you select the type of in-database machine learning services that you want to install. You can choose to use R, Python or both. Once SQL Server completes setup, you’ll be able to start using R or Python code as part of stored procedures.

Note: If you’re using SQL Server 2016, only R-Services (In-Data­base) will be shown at setup.

You can refer to for more information on setting up R and Python with SQL Server.

Enable external script. To use R or Python code in the stored procedure, you’ll need to configure SQL Server to allow external scripts. To enable external scripts, you’ll need to run the sp_configure and reconfigure commands (after the T-SQL code is successfully executed, you’ll need to restart the SQL Server service):

exec sp_configure  'external scripts enabled', 1
reconfigure  with override

After running the commands, you’ll see the following output:

Configuration option ‘external scripts enabled’ changed from 1 to 1. Run the RECONFIGURE statement to install.

Hello, AI! You’re now ready to do data science and AI with SQL Server. Using either Visual Studio, Visual Studio Code or SQL Server Management Studio, you can connect to the SQL Server instance with R or Python. You can run the code provided to make sure that R and Python are installed correctly. The code will output “Hello AI.”

Figure 3 Code to Ensure R and Python Are Installed Correctly

Code for R
exec sp_execute_external_script  @language =N'R', 
                        @input_data_1 =N'select ''Hello AI'' as txt' 
                        with result sets (([txt] nvarchar(8))); 
Code for Python
exec sp_execute_external_script  @language =N'Python', 
                        @script=N'OutputDataSet = InputDataSet',   
                        @input_data_1 =N'select ''Hello AI'' as txt' 
                        with result sets (([txt] nvarchar(8)));

If you need to install additional R libraries, you should set lib.SQL as the location of the SQL Server library. Similarly, if you need to install additional Python libraries, you can do a pip install of the relevant Python libraries and set the PYTHONPATH to point to where Python has been installed.

You can find the default locations of the R library files at :\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\R_SERVICES\library and Python library files at :\Program Files\Microsoft SQL Server\MSSQL14.MSSQLSERVER\PYTHON_SERVICES\library.

Let’s dive deeper into what’s in a stored procedure (with R code) that’s used for training a customer churn classification model. Figure 4 shows a stored procedure that trains a customer churn classification model by building an ensemble of decision trees using R. Figure 5 shows a similar stored procedure that trains a similar churn model using Python. Database developers leverage familiar skills of using T-SQL queries to select data from SQL server tables. The data is used as inputs to the R or Python code.

Creating a Stored Procedure to Train a Decision Forest Model Using R

CREATE PROCEDURE trainRDecisionForestModel AS
    execute sp_execute_external_script @language = N'R',
    @script = N'
      labelVar = "churn"
      trainVars <- rxGetVarNames(telcoCDR_Data_train)
      trainVars <- trainVars[!trainVars %in% c(labelVar)]
      temp <- paste(c(labelVar, paste(trainVars, collapse = "+")), collapse = "~")
      formula <- as.formula(temp)
      rx_forest_model <- rxDForest(formula = formula,
                                    data = telcoCDR_Data_train,
                                    nTree = 8, maxDepth = 32, mTry = 2,
                                    minBucket=1, replace = TRUE, importance = TRUE,
                                seed=8, parms=list(loss=c(0,4,1,0)))
      rxDForest_model <- data.frame(
        payload = as.raw(serialize(rx_forest_model, connection=NULL))); '
        @input_data_1 = N'select * from telcoCDR_Data_train'
        @input_data_1_name = N'telcoCDR_Data_train'
        @output_data_1_name = N'rxDForest_model'
      with result sets ((model varbinary(max)));

Creating a Stored Procedure to Train a Random Forest Model Using Python

CREATE PROCEDURE trainPythonRandomForestModel (@trained_model varbinary(max) OUTPUT) AS
  execute sp_execute_external_script @language = N'Python',
  @script = N'
    df = churn_train_data
    # Get all the columns
    columns = df.columns.tolist()
    # Specify the label column
    target = "churn"
    import numpy as np
    from sklearn.ensemble import RandomForestClassifier
    churn_model = RandomForestClassifier(n_estimators=20, max-depth=5)[columns], df[target])
    import pickle
    #Serialize the model as a binary object
    trained_model = pickle.dumps(lin_model) 
    @input_data_1 = N'select "TotalMinutesUsedLastMonth", "State",
      "CallDropRate", "UnPaidBalance", "TotalCallDuration", "TotalDataUsageMB"
      from dbo.telco_churn_data where Year = 2017'
    @input_data_1_name = N'churn_train_data'
    @params = N'@trained_model varbinary(max) OUTPUT'
    @trained_model = @trained_model OUTPUT;

Once the model is trained, it’s serialized and returned as varbinary(max). The model can then be stored in a SQL Server table.

The complete sample code for a customer churn model can be found at

With SQL Server 2017 CTP2, you can run Python code in the stored procedures. By supporting Python in SQL Server, it opens up new opportunities for you to use many of the deep learning toolkits (CNTK, TensorFlow and more), which provide Python APIs. In addition, the deep-learning toolkits enable you to specify the use of GPUs when training your model. You can now use SQL Server 2017 to perform intensive deep-learning jobs on text, images, and unstructured data that are stored in SQL Server, and then operationalize and do inference with SQL Server. Very exciting!

SQL Salary

The knowledge of SQL is highly coveted among companies that are involved in end-to-end app development, run high-traffic websites, or offer data migration solutions. Data from Indeed shows that the demand for SQL skills is growing steadily, up from 35.7 percent in 2017 to 42.7 percent in 2021. The escalating demand for SQL is only matched by the lucrative pay packages and benefits that SQL professionals enjoy.

Here’s a snapshot of SQL developer salaries across the world.

The average salary of an SQL Developer in the US is $87,973 per year.

The average salary of an SQL Developer in India is ₹4,38,815 per annum.

The yearly average salary of an SQL Developer based in the UK is £47,961.

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Final Thoughts

Regardless of whether you are a product manager, a web developer, an MBA, or a business analyst, learning the world’s most-used query language will boost your chances of landing a high-paying job.

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