sql assignment applied ai

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As a part of completing the coursework prescribed by the Applied-AI-Course , I have compiled all the latest work submitted by me on the Baymax classroom.

Optional Assignment

Initially 2 optional assignments were done:

• Python Practice Questions
• Pandas Practice Questions

Additionally, a mandatory SQL assignment was done.

Data Visualization

In the beginning of the course, Exploratory Data Analysis and Dimensionality Reduction assignments were done.

Supervised Learning

Later on, when diving into the course total 7 Supervised Machine Learning assignments were done:

• k-Nearest Neighbour
• Naive Bayes
• Logistic Regression
• SGD for Linear Regression
• Support Vector Machines
• Decision Trees
• Random Forest- Gradient Boosted Decision Trees

Unsupervised Learning

Also, Unsupervised Machine Learning assignments were done:

• k-Means, Agglomerative Hierarchical and DBSCAN Clustering
• Truncated Singular Value Decomposition

Deep Learning

Later on Deep Learning was done:

• Keras and Tensorflow

Supervised Learning Case Studies

Some Supervised Machine Learning Case Studies were also done:

• Quora Question pair Similarity
• Personalized Cancer Diagnosis
• Facebook Friend Recommendation

Work under construction..

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Applied Machine Learning Online Course

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Module 1: Fundamentals of Programming Chapters : 10 Assignments : 3 Completed : -->

Module 2: Data Science: Exploratory Data Analysis and Data Visualization Chapters : 10 Assignments : 1 Completed : -->

Module 3: Foundations of Natural Language Processing and Machine Learning Chapters : 12 Assignments : 5 Completed : -->

Module 4: Machine Learning - II( Supervised Learning Models) Chapters : 6 Assignments : 3 Completed : -->

Module 5: Feature Engineering, Productionization and deployment of ML Models Chapters : 3 Assignments : 0 Completed : -->

Module 6: Machine Learning Real-World Case Studies Chapters : 7 Assignments : 5 Completed : -->

Module 7: Data Mining(Unsupervised Learning) and Recommender Systems + Real-World Case Studies Chapters : 9 Assignments : 4 Completed : -->

Module 8: Neural Networks, Computer Vision and Deep Learning Chapters : 15 Assignments : 3 Completed : -->

Module 9: Deep Learning Real-World Case Studies Chapters : 7 Assignments : 2 Completed : -->

Module 10: Misc Topics: Spark for ML, Big Data, Reinforcement Learning, ML Design Chapters : 4 Assignments : 0 Completed : -->

Obective of Applied AI/ Machine Learning Online Course:

The AppliedAICourse attempts to teach students/course-participants some of the core ideas in machine learning, data science and AI that would help the participants go from a real-world business problem to a first cut, working and deployable AI solution to the problem.

Our primary focus is to help participants build real-world AI solutions using the skills they learn in this course. This course will focus on practical knowledge more than mathematical or theoretical rigor. That doesn't mean that we would water down the content. We will try and balance the theory and practice while giving more preference to the practical and applied aspects of AI as the course name suggests. Through the course, we will work on 15+ case studies of real-world AI problems and datasets to help students grasp the practical details of building AI solutions. For each idea/algorithm in AI, we would provide examples to provide the intuition and show how the idea to be used in the real world.

Key Points:

• 1. The validity of the Applied Machine Learning course is 365 days from the date of enrollment.
• 2. Expert Guidance, we will answer your queries in atmost 24hours.
• 3. 15+ real world case studies(includes 2 self case studies).
• 4. 30+ machine learning and Deep learning algorithms will be taught in this course.
• 5. There are no prerequisites for this course as we have taught everything from scratch including python programming.
• 6. We will also be conducting 70+ hours of live content based on students feedback and industry needs. We would cover topics to prepare our students better for real-world problem solving and interviews.
• 7. A mentor will be assigned to each candidate after the completion of 50% of the course assignments whose sole concentration would be on building the specific student's portfolio/resume and in interview preparation, mock interviews.

Target Audience:

• Undergrad (B.Tech/B.E/BSc statistics/Bsc computer science/BCA) students in engineering and science.
• Grad(MS/MTech/ME/MCA) students in engineering and science.
• Working professionals: Software engineers, Business analysts, Startup teams building ML products/services, Product managers, Program managers, Managers, etc.
• Data Analysts, Data Scientists, ML Scientists and ML engineers.

Course Features

Srikanth Varma Chekuri

QUALIFICATION: Masters from IISC Bangalore, PROFESSIONAL EXPERIENCE: 11+ years of Experience( Yahoo Labs, Matherix Labs Co-founder, and Amazon)

Free Videos Checkout our Free Videos Assignments : 1

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Pay easy monthly installments instead of lump-sum amount..

Learn AI for SQL

Print Cheatsheet

Prompts are what we input to a large language model, and prompt engineering is the art of optimizing prompts to produce effective output.

Guidelines for effective prompting include:

• use clear language
• understand and communicate the purpose of the prompt
• include important context
• provide examples to guide the AI’s output toward a specific format

Hallucination in AI

Hallucination is when an AI model generates outputs that are not based on real data but rather are fabricated by the model itself.

For example, an AI model might generate code using syntax that doesn’t actually exist.

Confirmation Bias and AI

As human beings, we are psychologically prone to believing new things that confirm our existing beliefs, known as confirmation bias . If we run code provided by an AI and it produces a result that “makes sense”, we are less likely to catch errors in the AI code.

We need to be very stringent with ourselves when using AI generated code, double-checking everything the AI has done for validity. Note that we can’t use AI to do these checks, as that is using the same flawed system to check itself.

Privacy and Security with AI

The data we feed into an AI model may be used to train future iterations of the model, or stored in some other way, which can result in data leaks where our information becomes part of the publicly available large language model. This poses privacy and security risks if the data is sensitive or confidential.

Conversational Prompting

Instead of writing one large detailed prompt, it is often more effective to interact with an AI system using a conversational approach, adding details as necessary and asking for refinements to build our analytics step by step.

By using a conversational approach, we:

• Avoid repeating the same information in multiple prompts.
• Save time crafting overly detailed prompts.
• Build the habit of reflecting and iterating on the AI output.

Here are some different types of prompts we might write during a conversation:

• Explain the goal ( I’m working on an analytics report about electric vehicles )
• Provide context ( I have three tables with the following names )
• State your request clearly ( Write a query to determine the most efficient vehicle )
• Review and refine ( Can you explain this piece of the query you generated: )
• Continuing the conversation ( That’s great, what other types of statistics should I look into for this report? )

Output Table Prompting

Instead of including every detail in our prompt, we can often get away with telling the AI the final output structure we want, and letting the AI infer how to structure the prompt.

For example:

• We described the three tables and how they join
• We used table.column notation to communicate which column belongs to which table
• We used a descriptive name for the third column (number_of_models) in the hope that the AI would calculate this column appropriately

Uses of AI for SQL

AI has many uses in SQL, including:

• generating queries
• brainstorming ideas
• explaining code
• debugging code

Learn More on Codecademy

Learn how to use ai for sql.

How AI & SQL Can Accelerate Your Data Analysis Workflow

• data analysis

Table of Contents

The Growing Role of AI Across Industries

1.   enhancing efficiency and accuracy, 2.   reducing human errors, 3.   managing big data efficiently, 4.   uncovering patterns in unstructured data, 1.   automated data cleaning, 2.   powerful, efficient queries, 3.   data visualization, 4.   pattern recognition and predictive analytics, 5.   unstructured data analysis, ai for data analysis: the final word.

Did you know that using AI for data analysis can be a total game changer for SQL developers and data analysts? In this article, we’ll learn about how AI can help you level up your data analysis skills and why it’s a must-have tool in every data professional’s toolkit.

Artificial Intelligence (AI) has emerged as a game-changer in the workforce, transforming industries and redefining how professionals deal with data. Specifically, the use of AI for data analysis is becoming increasingly critical in making informed decisions.

Before we get into the various use cases, let's clear up some common misconceptions about AI. AI should be viewed as a tool, not a threat. Contrary to popular belief, AI is designed to help us rather than replace us. Humans possess skills and abilities that are difficult (if not impossible) to replicate with AI; see why in our article 7 Reasons Why AI Won't Replace SQL Developers . As SQL developers and data analysts, we can leverage AI to overcome the limitations of traditional data analysis, improving our  efficiency and accuracy and helping us stay competitive.

If you're looking for a complete course that will take you from SQL zero to SQL hero while embracing AI, check out our SQL from A to Z learning track. This 7-course track covers both fundamental and advanced SQL abilities, giving you all you need to succeed in your career. So, let's dive in and see how AI can help you with your data analysis activities!

AI has grown across numerous industries, leaving a lasting impression on our world. It has become an indispensable resource for businesses seeking to make data-driven decisions.

For example, recent healthcare research highlights AI's significance in improving medical imaging for faster disease identification and diagnosis; this results in better patient outcomes. In the banking sector, AI excels at real-time analysis of massive datasets. This is critical to detecting fraud and analyzing risks, and AI’s accuracy is constantly improving. AI-powered recommendation systems, such as those used by Amazon and Netflix, can personalize product recommendations based on user behavior, improving consumer experiences and driving sales. These examples demonstrate the widespread impact of AI across a variety of sectors.

How AI Can Overcome Challenges of Traditional Data Analysis

As the world of data analysis advances, the challenges that traditional methods face become more obvious. Traditional methodologies are struggling to keep up with the sheer quantity and complexity of datasets. Because data is an essential resource for organizations to remain competitive, accurate and timely data analysis is critical. For more information on why data is an essential resource, check out our article Why Is Data a Valuable Resource for Your Business?

In response to the challenges of traditional data analysis, AI emerges as a powerful solution, transforming workflows and assisting professionals in their daily activities. Let's look at how AI can improve your workflow and productivity by overcoming the challenges of traditional data analysis:

Manual data cleaning, preparation, and analysis can take a significant amount of time and effort. In addition, manually conducting sophisticated data manipulations and calculations can also take a long time, thus producing delays in decision-making processes.

AI is a powerful solution to the time-consuming procedure of data processing. Let’s say a company has to analyze enormous amounts of customer feedback data to uncover trends and insights. Manual processing of this task could take days or even weeks.

Now, AI-powered natural language processing (NLP) algorithms allow data to be quickly reviewed; attitudes can be extracted and major themes discovered in hours or even minutes. This enables the team to make better-informed decisions faster and to focus on more complex analysis, resulting in increased efficiency and production.

Due to the volume and complexity of today’s data, human errors are nearly unavoidable in traditional data processing. Even the most experienced data analysts can make mistakes when cleaning, converting, or interpreting data.

These flaws can lead to incorrect conclusions and untrustworthy insights, jeopardizing the analysis's accuracy and integrity. The consequences of such errors can range from slight inconsistencies to serious financial or operational effects.

AI can also be used to reduce human error, particularly in operations demanding repeated or sophisticated data processing. In manufacturing, for example, AI-powered quality control systems can detect faults by evaluating product images in real time. They could use computer vision to detect any deviations from standard quality.

The risk of human error is reduced by automating this technique, resulting in greater product quality and increased efficiency. With AI handling repetitive and error-prone tasks, human workers can focus on creative and strategic areas, increasing overall productivity and quality.

The digital age has resulted in an explosion of data, which is commonly referred to as Big Data . Traditional databases and analysis tools may have difficulty working with the sheer volume and variety of Big Data, resulting in performance bottlenecks and lengthy processing times. Big Data management necessitates large storage and processing resources, which can lead to financial issues for enterprises.

Because AI can speedily analyze and process data, it is a game-changing tool for fast and efficient Big Data handling. Consider a retailer with millions of daily transactions. Handling and extracting insights from such massive amounts of data would be a massive undertaking, frequently leading to delayed decision-making.

By utilizing AI-powered data analysis technologies such as machine learning algorithms and natural language processing, this company can quickly find patterns, trends, and client preferences. Understanding consumer behavior enables the firm to optimize inventory management, develop focused marketing campaigns, and provide better customer experiences – thus allowing the retailer to remain competitive.

Unstructured data – such as text, images, and videos – holds immense potential for valuable insights. However, extracting patterns and correlations from unstructured data is a complex and challenging task for traditional data analysis methods. Human analysts must spend significant effort and expertise in preprocessing and transforming unstructured data into a structured format that’s suitable for analysis.

Instead, professionals can use AI to find patterns in this unstructured data. Imagine that our retailer wants to understand customers’ product opinions based on Internet reviews and social media remarks. Typical data analysis approaches would struggle to extract meaningful insights from these massive amounts of unstructured text data.

By leveraging NLP algorithms, the firm can analyze and categorize client sentiments, identify patterns, and discover common themes. As a result, they can make faster data-driven decisions, improve their products, and increase customer satisfaction – all based on the rich patterns buried inside unstructured data.

AI For SQL Developers & Data Analysts

By now you’re probably curious as to how AI can help your daily SQL development or data analysis responsibilities. Or if you’re simply interested in these careers, check out the article How to Become a Database Analyst or How to Become a SQL Developer . Now, back to how AI can help power up your SQL.

AI can automate data cleaning, a crucial step in the data preparation process. The detection and correction of errors, inconsistencies, and missing values in huge databases can be automated using AI-powered systems.

Consider a hospital that has a massive database of patient records. Data analysts can use AI to automatically find and correct anomalies in patient ages, addresses, or medical codes – ensuring that the data is accurate and reliable for future research. This saves time and effort. It also improves data accuracy, resulting in more precise insights and informed decision-making.

Using AI-powered query optimization techniques, we can improve SQL queries; this results in faster execution times and lower resource usage. This allows SQL developers and data analysts to more efficiently extract insights from vast databases.

Additionally, SQL professionals may use AI to save time, increase productivity, and gain deeper insights. This allows them to make more educated decisions and improve overall operations.

Consider a huge dataset with complex relationships and various dimensions. Manual visualization and exploration may be time-consuming and restricted if you want to discover its insights. By using AI-powered data visualization tools (such as automatic charting and dynamic dashboards), patterns and trends can be readily recognized.

AI can provide improved visualizations, making it easier for data analysts to interpret and convey their findings successfully. As a result their workflows will be accelerated, fostering deeper insights and allowing better communication of complicated data-driven stories with clarity and impact.

By leveraging AI-powered algorithms, patterns and relationships can be more easily identified in large datasets. Imagine you have sales data; by employing advanced machine learning algorithms, AI can analyze customer behavior rapidly. This includes such critical information as consumer purchasing preferences, seasonal trends, and peak sales times.

With this knowledge, marketing strategies can be enhanced, future sales forecasted, and better data-driven decisions can be made. In short, the business can more efficiently meet customer requirements.

AI's ability to analyze text, image, and video data opens up new possibilities for data-driven decision-making. Consider a healthcare organization that collects patient input via a variety of methods, such as written notes, audio recordings, and photographs. Manually analyzing this unstructured data would be time-consuming and impractical.

Using NLP and AI picture recognition technologies, the hospital can quickly extract useful insights from this data. AI can classify emotions, recognize recurring themes, and even spot patterns in medical imagery. As a result, a full picture of patient experiences can be found. This helps hospital administrators identify areas for improvement and make better data-driven decisions that improve the overall service quality.

We've looked at a variety of AI use cases for SQL developers and data analysts in this article, emphasizing the growing role of AI for data analysis. However,  bear in mind that these are only a few examples of how AI could improve our data analysis operations. AI's rapid development continues to show new potential and applications for our day-to-day responsibilities.

Professionals that want to stay competitive must stay up to date on the latest AI trends and discoveries. Embracing AI as a productivity booster and useful tool in your data analysis journey will yield considerable long-term benefits. In today's data-driven environment, AI is not a threat but a valuable ally, complimenting human skills and propelling us to success!

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Here's how the SQL AI tool was born: I often write SQL queries for work, but it can be really tedious and time-consuming. First I have to think about how to even approach the query, and then I have to google stuff to fix issues and refresh my memory.

I started using ChatGPT for help, but it was annoying to have to explain the tables/views every time.

To fix this, I built a tool that remembers your whole schema. It gives you a query to extract all the necessary info in one go and then you just copy-paste it once (it's saved with encryption). Then, all you have to do is write what you need in plain English, Ex. "Users who have been online over 5 days this week", and it writes the SQL query for you.

I showed it to my colleagues and they went crazy and are obsessed with it, as are my ex-colleagues from my last company.

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Generate SQL Queries from Natural Language Using Oracle Autonomous Database Select AI

AI solution topics

Introduction

Prerequisites and setup, getting started, additional resources.

Complex problems often are resolved by integrating multiple technologies. This solution explores the integration of cutting-edge AI technologies, including Cohere AI with Oracle Autonomous Database Select AI, to craft responsive SQL queries or engage in conversational interactions. With the aid of the DBMS_CLOUD_AI package, we configure the translation of natural language prompts into SQL statements, seamlessly bridging the gap between user input and database interaction.

Oracle APEX is used to build a user-friendly interface, letting users easily input queries and visualize AI-generated results. Additionally, we leverage Oracle Machine Learning Notebooks to execute Select AI SQL queries, further enhancing our analytical capabilities.

This solution showcases a practical example of Cohere AI’s synergy with Oracle Autonomous Database , using customer information and hospital data tables to illustrate the potential of AI-driven database interactions.

• Oracle Cloud account— sign-up page
• Oracle Cloud Infrastructure— documentation
• Cohere developer guide— documentation
• Oracle APEX— documentation
• Oracle Autonomous Database— documentation
• Detailed steps and sample code on GitHub
• Blog: Natural Language to SQL Generation on Autonomous Database

• Announcements
• Best Practices
• Thought Leadership
• SQL Server 2022
• SQL Server 2019
• SQL Server Management
• SQL Server on Azure VMs
• SQL Server on Linux
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• Azure Synapse Analytics
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Getting started with delivering generative AI capabilities in SQL Server and Azure SQL

• By Bob Ward, Principal Architect, Microsoft Azure Data

AI is transforming everything we do, including how we interact with data. Data is the fuel for AI. Microsoft SQL Server and Azure SQL is the data platform to power today’s modern applications with security, performance, and availability, but also have capabilities and support scenarios required in the era of AI.

Azure SQL and SQL Server support building new generative AI experiences that become supercharged when combined with your data. In addition, SQL brings AI assistance to a new level with copilot experiences for both self-help and natural language to SQL capabilities.

In this blog post, I’ll share how you can get started with these new AI experiences —Azure SQL and SQL Server. First, check out our latest story on Microsoft Mechanics:

Responsible AI

Many conversations about AI starts with a statement on responsible AI . Microsoft has established a set of policies, research, engineering efforts, and principles to ensure AI technologies are adopted, implemented, and used in a responsible manner.

These principles include fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Your data is your data. One promise for Microsoft is that private data of any user, including prompts and responses, are never used to fine tune a model that Microsoft hosts or implements.

Generative AI applications with your data

One of the motivations for generative AI applications is to become more productive, creative, and efficient through the generation of content in all forms: text, audio, and video. Many of today’s examples for generative AI applications involve the user of a natural language prompt and the interaction with a language model . Many of you have probably at some point used an application like ChatGPT or Microsoft Copilot which are great examples of generative AI applications.

Get smarter with your data

While these are great applications, they don’t know about your data. The combination of a generative AI application with your data, for example, stored in a database, can be quite powerful. Generative AI provides methods for smarter searching on your data. A common application pattern is to use language models with a prompt application to “chat with your data.” Using the concept of vector embeddings , language models allow you to get more precision on questions about your data. In addition, responses to questions are more tailored to your users and searches can often be faster because language models allow you to use the power of natural language. Generative AI applications with your data provide unique intelligence in an interactive manner, including conversations. Language models are trained to provide more context on your search, often giving you more (hence generated) content than you might normally get using common searching techniques within a database engine with a language like SQL.

Prompt engineering, Retrieval Augmented Generation fundamentals, and hybrid search

As you investigate how you can take advantage of generative AI with language models, there are two important concepts to understand:

Prompt engineering is the discipline of using high quality and descriptive prompts when interacting with a language model. The concept is simple. The better the prompt, the likelihood of a better response from the model. For example, let’s say you use Microsoft Copilot and type in a prompt like “ What are the best steak restaurants in Fort Worth, Texas? ” You will get a good list of steak houses in Fort Worth, Texas based on a search by Copilot of rankings across a broad set of searches. But what if you are on a bit of a tight budget? Instead of looking at the results from the prompt and trying to figure out what prices you can afford you could instead ask “ I’m on a tight budget but want to eat at a good steakhouse in Fort Worth, Texas. ” Now your results are more tailored for what you really want. And since you are interacting with a language model, it understands the phrase “tight budget” means you need choices that are good but affordable.

While this technique can be great if you are interacting with a model that is trained to help you search the internet, what about your own data? One prompt engineering technique to get smarter with your data is called Retrieval Augmented Generation (RAG). The concept of RAG is to search for information from a source of data and use those results to augment the prompt to the model. For Azure SQL and SQL Server, this could mean using standard SQL techniques to search for data using Transact-SQL (T-SQL), taking these results, and sending them along with the original prompt to the language model. This technique is simple and can be an effective way to get smarter with your data, and this can work with almost any type of data you search, not just SQL databases.

For Azure SQL and SQL Server, a more sophisticated technique is called hybrid search . With hybrid search, you can use the power of vector search combined with the query capabilities of your SQL data. Vector embeddings are numerical representations of data that capture semantic meaning and similarities. The key to embeddings with language models is that the model can generate embeddings based on data like text. This means you can take text data inside your SQL database and use a model to generate embeddings and then store these embeddings in your database. Now anytime you want to search for data inside the database, you can send a prompt to a language model which will generate embeddings for the prompt. And then you can use vector search techniques to compare the embeddings from the prompt with the embeddings stored in your database. You can then combine the vector search with other techniques you would normally use in T-SQL to find data in your database: a hybrid search.

There are methods today to use hybrid search completely inside the engine using T-SQL and outside the engine using Microsoft Azure AI Services or frameworks like LangChain or Semantic Kernel.

Get started quickly with Azure AI Services

One approach to get started quickly with no code required is to index your SQL database using Azure AI Search and then use Azure OpenAI Service to build a simple prompt app and “chat with your data” using a hybrid search technique.

You can use Azure AI Search to build an index based on a table in your SQL Server or Azure SQL database. When you build the index, you can apply a skillset to generate embeddings based on your data and store the result in the index. Now you can use Azure OpenAI with a prompt application to perform hybrid searches on your data. One example prompt application to perform simple testing is to use Azure AI Studio . In addition, as you change your SQL data, the index is automatically updated including the embeddings. The figure below shows the basic flow:

You can see this in action from the latest Microsoft Mechanics video or download a deck with demo recordings. One of the interesting aspects of this example is the method of changing the system message to direct the language model to respond in a unique way using the same data . This is also a great example of prompt engineering.

Learn more about Azure SQL in Azure AI Search .

Use hybrid search inside the engine with T-SQL

Let’s say instead of using a separate index, you would like to build generative AI capabilities for your application all inside the engine using T-SQL. You can do this in a very powerful way for Azure SQL Database today using a combination of vector embeddings, vector search, and other T-SQL search methods. This is a true hybrid search because you are using all the power of the SQL query processor together with a vector search. An example my colleague Davide Mauri has developed uses these techniques to help him find the best restaurant for one of this favorite Italian foods, focaccia bread.

Davide built an application that stores reviews from restaurants in the form of vector embeddings using Azure OpenAI Service with Azure SQL Database Representational State Transfer (REST) API inside the engine. With this in place, he can take any prompt to search for the best focaccia bread and use the same technique to generate embeddings for the prompt. Then, he can use a new T-SQL vector_distance function to perform a similarity search. The true power of SQL is possible because Davide built queries to combine this vector search with other criteria from spatial types, the new JSON data type, and the new Regular Expression (RegEx) T-SQL capabilities.

You can see a diagram of how these techniques are combined together below:

You can see this demo in action in our Microsoft Mechanics video or download a deck with demo recordings. You can learn more about the new JSON data type (preview) . You can also sign-up to preview the new vector search capabilities and RegEx in Azure SQL Database .

Building generative AI applications using frameworks

There are other methods to build generative AI applications with Azure SQL and SQL Server using frameworks such as:

• LangChain : LangChain is an open-source framework to orchestrate AI applications with language models. You can use programming languages such as Python and JavaScript to build your own generative AI application. LangChain supports the SQL Agent Toolkit which allows you to interact with a SQL database using natural language prompts. The toolkit integrates the connection to your database with a language model to generate SQL queries based on natural language prompts. You can see an example of this in the blog post “ Building your own DB Copilot for Azure SQL with Azure OpenAI GPT-4 .”
• Semantic Kernel : Semantic Kernel is an open-source SDK to allow you to build AI applications in C#, Python, and Java, interfacing with many common models in the industry such as OpenAI, Azure OpenAI, and Hugging Face . A library has been built to allow a Semantic Kernel application to interact with Azure SQL Database (and use the new vector search capability) called the SQL Connector .

See a full range of SQL and generative AI examples .

The age of copilots

Microsoft has transformed the industry and how we work and live with a new set of AI assisted experiences called Microsoft Copilot. Copilots are AI companions that work everywhere you do and intelligently adapt to your needs.

Use Copilots where you live

I realize there seem to be copilots everywhere. It is hard to keep track. Microsoft is investing in Copilot experiences in almost every product or service. Use the product or service you normally do and see what Copilot can offer. For example, if you have Microsoft 365, use Copilot for Microsoft 365 naturally within Microsoft Teams or any Office product or service. I personally use Microsoft Copilot in my Edge browser or on the app on my phone for any search experience I need today—web or work related.

Microsoft Copilot in Azure

The primary resource to manage and explore Microsoft Azure is the Azure portal. You can now use Microsoft Copilot in Azure within the Azure portal to manage, deploy, and troubleshoot Azure resources. Azure SQL Database is one of the most popular Azure resources in the world, so we have built two distinct experiences within the Copilot in Azure framework using natural language for self-guided assistance and T-SQL query authoring:

Microsoft Copilot in Azure integration

One of the strengths of SQL Server is the deep built-in telemetry within the engine all accessible through T-SQL. This includes Dynamic Management Views (DMV) and Query Store. These rich, traditional capabilities shine through now in Copilot. For example, you can prompt with Copilot a general statement like “My database is slow” and Copilot, based on your permissions, will access real-time diagnostic data, in the context of your database, to help you quickly navigate difficult, and often vague, performance problems. Here is an example:

You can then continue a conversation with Copilot to tune the query causing the problem. There are many different skills that Copilot can help you all in the context of your database. Learn about all the possibilities of Copilot skills in Azure SQL Database (preview) .

Natural language to SQL

The T-SQL query language has so many great capabilities and possibilities. But the open nature of T-SQL also leads to difficulties in crafting queries to meet the need of your application. Along comes a copilot experience to allow you to “chat” with your database using natural language in the context of your database and schema: table, columns, and key relationships. A simple example is being able use a natural language statement to generate a query that typically requires several joins over multiple tables like the following:

Learn more how to use natural language to SQL .

You can see both experiences in action in our Microsoft Mechanics video or download a deck with demo recordings.

Innovations moving forward

We are just beginning with SQL and AI. We have innovations for the future planned for enhancements with AI services, enhancements for deep integration for vector search, and enhanced Copilot experiences for SQL Server. Stay tuned for future blog posts showing all of these innovations.

Learn more today

Here are more resources for you to learn more about SQL and AI:

• Explore the capabilities of Azure SQL Database .
• Keep track of SQL and AI at our one-stop documentation hub .
• Keep track of all of our SQL and AI examples .
• Showcase your own demos using our decks with demo recordings built-in .
• Read our team’s blog on developer experiences for SQL including the latest AI examples at Azure SQL Devs’ Corner .
• To see all this in action, check out our new Microsoft Mechanics video .
• Learn the essentials to building a SQL generative AI application .
• Check out the comprehensive blog post from Sanjay Mishra to explore the power of generative AI applications with Azure SQL Database.
• Learn more about Copilot in Azure SQL Database experiences from Joe Sack’s Data Exposed episode.
• Power AI apps and develop rich experiences with Azure SQL Database
• The power of AI and Copilot for Azure Databases

Migrate, modernize, and innovate with the modern SQL family of cloud database services

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Top 5 SQL AI Tools to Ease Writing SQL 2023

If you work with data and databases, SQL AI tools can significantly improve your productivity and efficiency. Say you are a data analyst, data scientist, developer, or DBA, wouldn't it be great to have SQL queries generated automatically from natural language input (perfect for data analysts!), or have query performance optimized? If you want to start learning SQL, this is also an excellent way to get started.

Here we gathered some of the most popular SQL AI tools on the market.

AI2sql.io #

AI2sql is an AI-driven SQL query generator, which means you can use natural language to instruct AI2sql to complete SQL queries. AI2sql has been around since 2021, way before the AIGC wave arrived. Recently, it has also integrated with OpenAI's GPT-3. Compared to ChatGPT, AI2sql is designed explicitly for querying databases or generating SQL queries. It supports the most popular databases on the market such as MySQL, PostgreSQL, MongoDB, Oracle, etc.

Product Hunt really is the go-to place for product releases, all tools featured today was submitted to Product Hunt at some point, let's also take a quick look at how they performed.

AI2sql's first launch in 2021 on Product Hunt ranked fifth for the day (which must have been quite impressive at the time). Two years later, version 2.0 was introduced with ChatGPT capabilities and received 142 Upvotes.

AI Query uses the GPT-3 model to generate SQL queries from natural language. It currently supports database types such as Postgres, MySQL, MariaDB, and SQL Server, with plans to include others in the future (taking notes from this genius marketing approach).

What sets it apart from other AI tools that help you write SQL is its visual interface, which allows you to define the structure of database tables directly.

AI Query was launched on Product Hunt just one month after ChatGPT was released (at the end of November 2022).

Outerbase #

Outerbase was released earlier this year and can be used on top of Postgres, MySQL, and most relational databases. Outerbase has a super modern and minimal interface compared to traditional database management tools. The user experience is similar to that of a spreadsheet. Although it also has AI-enhanced capabilities to assist with writing SQL and gaining insights from the database, it feels more like a SQL client designed for data analysts and business users.

Since its release on Product Hunt and Hacker News earlier this year, they haven't updated their Changelog since April, nor have there been updates on their social media channels. We wonder what happened?

SQL Chat brings SQL clients from the traditional GUI-based stage to the CUI (Chat-based UI) stage: it integrates with ChatGPT to help you write SQL (and of course, answer any questions about databases!). It currently supports MySQL, PostgreSQL, SQL Server, and TiDB Serverless. And if you connect SQL Chat to your own database, it can write more accurate SQL queries.

Recently, SQL Chat introduced a subscription model (If you don't have an OpenAI account, you can still enjoy SQL Chat!) and the option to choose between GPT-3.5 or GPT-4 models.

Since its launch on Product Hunt, SQL Chat has gained significant attention.

TEXT2SQL.AI #

Supply your database schema to Text2SQL.AI , choose the database you are using (MySQL, PostgreSQL, Snowflake, BigQuery, MS SQL Server), and Text2SQL.AI will generate the corresponding SQL for you.

When compared with other SQL AI tools, their website that besides being affordable (current price is $4/month for 300 requests), it is also the only tool capable of generating SQL as well as regular expressions, Excel and Google Sheets formulas (again, perfect for business analysts who need to work with Excel and data all the time!).

AI products are all over the place this year, but Text2SQL.AI was the #1 Product of the week in the AI category on Product Hunt when it was released.

What We Think #

SQL AI tools are the best: they can improve efficiency, save us time and effort, and make it easier to interact with databases. However, they are not perfect. Although they also use state-of-the-art natural language processing models, the results may sometimes be inaccurate. And if you want to use them in production, make sure to double-check before you hit RUN.

On the other hand, if you are not ready to jump to the AI ship, below are the traditional options for you:

• Top Open Source SQL Clients
• Top Postgres GUI client
• Top MySQL GUI client
• Top Database Schema Migration Tools

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