The Research Nest’s Post

🧑💻 Start building generative AI apps with your data in SQL Server and Azure SQL. Learn more about our latest innovations, including enhanced Copilot experiences for SQL Server: https://msft.it/6041YC5k1

As Build 2024 approaches, the Azure SQL Database team is preparing a great line-up of product announcements. Innovation continues to be a big theme as we prepare to showcase exciting experiences powered by SQL data and generative AI. The ideal starting point for the new generation of AI-ready SQL ap...

🚀 Exciting Update: Azure SQL Database as a Vector Database Sounds amazing, right? Now, there's no need to switch to a dedicated vector database for storing embeddings when all your data already resides in an SQL database. I’ve put together a detailed document explaining how to seamlessly integrate your RAG (Retrieval-Augmented Generation) applications with Azure SQL Database. This innovation simplifies workflows and makes managing embeddings more efficient within your existing database setup. 👏 Kudos to Davide Mauri and the incredible Microsoft SQL Database Team for making this possible! Let me know your thoughts or reach out if you'd like to explore this further. 🔗 to know more check this repo - https://lnkd.in/gPi_SnFN don't forget to read this- https://lnkd.in/g49QRyGw #azure #azuresqldatabase #ai #rag #vectorstore #pinecone #embeddings #llm #generativeai

Checkout my latest blog post and Data exposed video on topic of “Similarity Search with FAISS and Azure SQL”! 🎉 In this comprehensive guide, I dive into how you can leverage data stored in Azure SQL for efficient similarity searches using FAISS (Facebook AI Similarity Search). Whether you’re a data enthusiast or a seasoned professional, this tutorial will help you implement search capabilities in your projects. Plus, the code works seamlessly in notebooks within #MicrosoftFabric. 🔗 Read the full blog post here: https://lnkd.in/g72EG2ea 📺 Watch the YouTube video here: https://lnkd.in/gucKkSky 💻 Check out the sample code here: https://lnkd.in/gaS5spjF Feel free to experiment with different FAISS indexes and encoding models to optimize your searches. Ever wondered what movies are similar to your favorite heist film or a mysterious murder in a small town? With FAISS and Azure SQL, you can find out! 🎬🍿 Dive into the world of movie searches and discover hidden gems that match your queries. Happy searching! 🚀 #DataScience #MachineLearning #FAISS #AzureSQL #SimilaritySearch #TechTutorial #AI #AIApplications

Databricks Notebook Experience: Pros, Cons, and Neutral Observations, Part 1. Pros: -The ability to easily leverage different languages within the same notebook is very powerful. While you should try to stick to one language whenever possible, being able to find ways to overcome a language's deficiencies by using another language means that you almost always find a solution to complex problems. -The UI is solid, with menu items located where you would naturally expect them to be. -The UI integration of the Databricks AI Assistant is very well done. It is accessible but not overpowering. Love the ability to click "Diagnose error" and leverage the AI to identify what went wrong. -Love the ability to use both all-purpose compute to work with any supported language as well as SQL Serverless Warehouses for SQL only workloads, all within the same dev experience. -The new results table allows you to filter on values post-query, which is helpful when you do ad-hoc queries and need to dig a little deeper without having to update your code. -Table of contents works very well for organizing code with Markdown. Neutral: -The ability to add tabs with visualizations is nice, but feels a little out of place in an interface that should in theory be code heavy. If perhaps Databricks was converting those visualizations back into code and providing it to end-users, it would make more sense. -Though the post-query filters are fantastic, I tried to see if the Databricks AI Assistant would turn those into code with a simple prompt, and while it was able to pull up the fields that I was filtering by into a WHERE clause, it did not pick up the values I was filtering on. -I am not a fan of the ability to schedule a 1-task workflow within Notebooks. I've seen it lead to bad practices and workflow confusion. If it easily allowed users to add the notebook to an existing job as well as prevented users from scheduling it with AP compute, it would be better. Con: -In the SQL Editor, you can choose to export tabular results to CSV, Excel, etc, while in the Notebook experience, "Table" results are only exportable to CSV. Which then brings me to the next point... -In tabs with visualizations, you get the option to download the results to CSV/Excel/TSV, however, you don't actually get a chart, but a tabular version of the results, even when exporting to Excel. I would expect an Excel native chart, but that's not what I get. -You can add tabs to dashboards, except, these are not THE "Dashboards" as in Lakeview Dashboards and traditional SQL dashboards, but, dashboards that live within the Notebook, which is all sorts of confusing. -The integration of UC's comments / other information from it as a hover-over on catalogs/tables/fields is missing, just like in the SQL Editor. HUGE missed opportunity that discourages UC feature adoption. Hope you find this helpful, and would love to hear your thoughts on what I wrote and your experience!

Exciting news for those looking to supercharge their databases: MariaDB 11.7 is here, and it brings vector search to the table! 🔍💡 If you're using MariaDB or MySQL and are ready to level up your search capabilities, MariaDB 11.7 offers powerful new features that make it easier to perform fast and efficient searches on high-dimensional data, perfect for AI/ML, recommendation systems, and more. 👉 Why upgrade to MariaDB 11.7? Vector Search: Unlock fast, similarity-based search for large datasets. Improved Performance: Enhanced indexing and query optimization. Full Compatibility: Stay seamlessly connected with your MySQL environments. 🔧 Whether you're working with machine learning models, recommendation engines, or anything requiring large-scale data search, this new feature is a game-changer. 💥 Ready to take your database to the next level? Upgrade to MariaDB 11.7 today and try out the power of vector search. Let your data work smarter for you! 👉 Get started here: https://lnkd.in/d27cAWn7 #MariaDB #MySQL #VectorSearch #AI #MachineLearning #DatabaseUpgrade #TechInnovation #DataScience #DatabaseManagement #OpenSource #MariaDB117 #database

📣📣 Announcement ! 📣 📣 Why maintain a separate Vector database when your favorite Azure SQL Database can seamlessly accommodate vector embeddings? We are super excited to announce the Early Adopter Preview of Native Vector Support in Azure SQL Database 😎 and currently accepting requests from customers who wish to participate. Details on the feature and how to signup are in the blog below! By integrating vector search into Azure SQL, you simplify application development, coexisting with operational data for efficient similarity searches, joins, and aggregations—all while leveraging Azure SQL’s sophisticated query optimizer and robust enterprise features CC: Davide Mauri Abhiman Tiwari Rajkumar Iyer Muazma Zahid Sanjay Mishra Parth Shah Krithika Subramanian #AzureSQL #AI #MSBuild #Vectordatabase #Embeddings #AzureOpenAI

So many cool and interesting features announced at #MSBuild for #AzureSQL. Make sure to check them out. This slide that Muazma Zahid put togheter is a great starting point. JSON, RegEx, Vectors, GraphQL, AI...oh my!!!! 🔥 🔥 🔥

Discover the integration between FAISS (Facebook AI Similarity Search) and Azure SQL, designed to enhance your data analytics capabilities. 📊 What is it ?🤔 FAISS, combined with Azure SQL, enables high-speed similarity searches on large datasets, streamlining the process of deriving insights from your data. How to Implement:🛠️ 1. Set up your Azure SQL environment. 2. Integrate FAISS for efficient vector searches. 3. Leverage Azure's robust security and compliance features for a seamless experience. Impact and Benefits:🌟 1. Lightning-fast Vector Searches: Achieve personalized recommendations with ease. 2. Enhanced Data Analytics: Boost your machine learning capabilities. 3. Seamless Integration: Enjoy a unified experience with Azure's security and compliance features. This integration is set to transform data interaction and insight generation. Learn more about this development here: https://lnkd.in/gW6k8tMg #DataInnovation #AI #MachineLearning #TechNews

MySQL Rockstar 2023: Q&A with Alkin Tezuysal " #MySQL stepping into the vector realm is not only crucial but inevitable. I foresee vector data types evolving regarding indexing, storage, compression, and encryption techniques, all of which will be essential as AI/ML applications grow." https://lnkd.in/eTCjcwYR #database #databases #AI #VectorDatabase #MachineLearning