#DailyLearning-November [Day 4/20]#20DaysOfLearningChallenge 🗓 **Today's Focus:** Sliding Window Problems & MongoDB Aggregations 💡 **What I Learned Today:** 1. **Sliding Window Technique**: I solved five problems using the sliding window technique, focusing on optimizing subarray operations and string manipulation: - [Longest K Unique Characters Substring](https://lnkd.in/gj8yUacW) - [Minimum Window Substring](https://lnkd.in/g-EYU544) - [Fruit Into Baskets](https://lnkd.in/g7xBPuuq) - [Longest Substring Without Repeating Characters](https://lnkd.in/gYpbRtP4) - [Substrings of Size Three With Distinct Characters](https://lnkd.in/gxsVs6RK) 2. **MongoDB Aggregations**: Explored aggregation concepts using **Masai Prepleaf** resources. - **Aggregation - 1**: Basics of grouping, filtering, and projecting data. - **Aggregation - 2**: Advanced operations like `$group`, `$lookup`, and `$unwind`, enhancing my ability to query and analyze complex datasets efficiently. 🚀 **How I Applied It:** - Practiced solving problems requiring efficient window management and string operations to improve my algorithmic thinking. - Worked on MongoDB aggregation pipelines, applying what I learned to analyse structured and unstructured data. 📈 **Next Steps:** Tomorrow, I’ll dive deeper into MongoDB queries and explore advanced Express concepts like middleware chaining. 🔗 **Resources Used:** - [Masai Prepleaf](https://www.prepleaf.com) - GeeksforGeeks - LeetCode 🔗 You can check my work at : https://lnkd.in/dTx6v-6a https://lnkd.in/gBi4h7CE #Masai #Prepleaf #DailyLearning #SlidingWindow #MongoDB #ProblemSolving #CodingSkills #WebDevelopment Masai Prepleaf by Masai
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🌟 Live RAG Comparison Test: Pinecone vs. MongoDB vs. Postgres vs. SingleStore ❓Why It Matters: 🔹The role of Retrieval-Augmented Generation (RAG) has become increasingly significant in today's #GenAI landscape. RAG combines the best of both worlds of generative and retrieval-based models to provide more contextually accurate and rich responses in LLM apps. 🔹Latency is crucial for RAG in LLM apps because it directly impacts the user experience by determining how quickly and interactively the model can retrieve and integrate relevant information to generate accurate responses 🔹Rohit Bhamidipati will offer a hands-on RAG performance evaluation of leading back-end/database vendors such as Pinecone vs. MongoDB vs. Postgres vs. SingleStore, comparing throughput, efficiency, and latency for large datasets. 📚 What You'll Learn: ➡️ The mechanics of RAG and its impact on enhancing language model responses. ➡️ How Vector Databases like Pinecone, MongoDB, PostgreSQL, and SingleStore facilitate the functionality of RAG. ➡️ Comparative analysis showcasing real-time performance metrics of these databases. ➡️ Best practices for integrating these technologies into your AI and ML projects to boost efficiency and accuracy. ⚡️ Can't make it? No worries! All registrants will receive a copy of the webinar recording and additional resources via email post-session ✅ Live demo and code-share session will be offered for a hands-on experience 📅 Event Details: 🔹 Thursday, May 9 @ 10-11 AM PDT 🔹 #Free Registration: https://lnkd.in/g6EvB8NG #artificialintelligence #rag #ad
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Introducing QueryGen, our latest open source project, Mongodb Query Engine using LLM! 🎉 We noticed a lack of NoSQL options for data analysis and visualization, so we created this powerful query engine for MongoDB. 💪 🌟 Features: 📊 Dynamic dashboard for real-time analytics 🔧 Compatible with LLMs supporting function calling 🚀 Set up for Mistral Codestral and GPT4 Turbo 🌐 Open source and ready for the community Our marketing team is already benefiting from this user-friendly tool. 📈 It provides natural language interface to query aggregate pipelines dynamically without depending on back end team to write manual queries. We're excited to see how developers will adapt and expand upon it. 🔓 🤝 Get Involved: ⭐ Star the repo on GitHub 🍴 Fork and build custom features 🐛 Report issues or suggest improvements 🙌 Spread the word Let's see what you'll create with QueryGen! 💻 Repo Link: https://lnkd.in/gvzaqbQW Special thanks to team at Qolaba & Dhruv Thummar to make this happen #QueryGen #Qolaba #opensource #mongodb #llm #developers #analytics #NoSQL #DataVisualization #AI
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🔍 Mastering Mongoose Operators! 🔍 I’m currently diving deep into Mongoose to manipulate data efficiently using powerful operators like $lt, $lte, $eq, $gt, $gte, $in, $nin, $and, $or and more. Learning how these operators work has really opened my eyes to the versatility of MongoDB when handling complex queries! 📊 I’m also getting familiar with regex for pattern matching – super useful for handling text-based data. Excited to keep sharpening my skills! 💡 #Mongoose #MongoDB #DataManipulation #BackendDevelopment #Regex #LearningJourney
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🚀 Day 16 of my #DailyLearningCheck at Masai School (26 September) After resolving some technical issues, I’m back to posting my daily learning updates. Excited to get back on track and continue sharing my journey! 🎉 Today's session was a deep dive into advanced MongoDB features. We covered: 🔹 Regular Expression: Using regex in MongoDB to make text-based queries more efficient. 🔹 Indexing: Implementing indexing for faster query performance, which is a game-changer for database speed! 🔹 Database Logs & Profiler: Monitoring and analyzing database performance using MongoDB’s logging and profiler features to optimize queries. 🔹 Capped Collections: Working with collections that maintain a fixed size — perfect for high-throughput use cases like logging! 🔹 Schema Validation: Ensuring data integrity by enforcing schema validation rules in MongoDB. I’m ready to put these concepts into practice and enhance my database management skills. Onwards and upwards! 💪 #MongoDB #DataOptimization #DatabaseDesign #MasaiSchool #NoSQL #Indexing #SchemaValidation #CappedCollections #RegularExpressions Masai Ritu Bahuguna
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𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗔𝗜 𝗮𝗽𝗽𝘀 𝗰𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟? 🐘 🤖 Now everyday application developers with no specialized AI/ML background can build AI apps with PostgreSQL. Thanks to its robust relational foundation and ecosystem of extensions like pgvector, pgvectorscale, and pgai, PostgreSQL is all you need to build a state of the art AI application. Just like the timescaledb extension turned Postgres into a time series database, now the pgvectorscale and pgai extensions have turned Postgres into a vector database. Here are some AI systems you can build using PostgreSQL, with resources to learn more: ➡️ RAG: https://lnkd.in/gg-T6MQ7 ➡️ Search: https://lnkd.in/gY5KYnaY ➡️ Agents: https://lnkd.in/g6nGsY9n ➡️ Text to SQL: https://lnkd.in/grJtr_9c ➡️ Recommendation Systems: https://lnkd.in/gvpN_dzc No wonder PostgreSQL is catching fire as the default database choice for AI applications! That’s something covered in detail in our recent webinar: https://lu.ma/0v7nwfxd #PostgreSQL #Timescale #ArtificialIntelligence #AI #Vectors #LLM #RAG #SQL #Postgres #SQL
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🚀 Day 18 of my #DailyLearningCheck at Masai School Today, I focused on solidifying my understanding of the MongoDB concepts I've learned over the past two weeks: 🔹 Lookup Queries: Solved several challenging questions on lookup, which helped me get more comfortable with aggregating data from multiple collections. 🔹 Revision: Spent time revisiting all the topics I’ve learned in the past two weeks, including database design, indexing, schema validation, and more. It’s been an intense learning journey so far, and the revision has really helped reinforce these important concepts. #MongoDB #DataAggregation #LookupQueries #DatabaseDesign #NoSQL #MasaiSchool #DailyLearning #RevisionIsKey Masai Ritu Bahuguna Snehil Gupta
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Exciting news! Just wrapped up an immersive Session with Learnbay , mastering the art of SQL and MongoDB. Here are some key takeaways from each module: SQL: Mastered the art of structured querying, enabling me to retrieve and analyze data efficiently. From basic commands to complex queries, I'm now adept at navigating and extracting insights from databases seamlessly. MongoDB: Explored the dynamic realm of NoSQL databases, gaining proficiency in managing unstructured data. The flexibility and scalability of MongoDB have opened up new possibilities for handling diverse data types and structures with finesse. #Learnbay #DataScience #AI
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🚀 PostgreSQL: The Future of Databases! 🚀 PostgreSQL has always been a powerhouse for handling relational data, but its capabilities have reached new heights with cutting-edge extensions like Apache AGE and pgvector. 🌐✨ Imagine this: a single database platform that seamlessly integrates both graph-based queries AND vector embeddings for AI and machine learning applications. The possibilities are endless! Here's what you can do with this dynamic duo: 1️⃣ Apache AGE transforms PostgreSQL into a graph database, making it perfect for handling complex relationships and networks, like: Social networks 🧑🤝🧑 Supply chain management 📦 Fraud detection 🔍 2️⃣ pgvector brings AI into the mix by enabling vector similarity searches directly inside PostgreSQL. This is a game-changer for: Semantic search: retrieving the most contextually relevant content Recommendation systems: using embeddings to suggest the best matches Image and document retrieval: finding similar content using deep learning models 📸📄 💡 When you combine graph capabilities from Apache AGE with the power of pgvector, you're looking at a robust system that can handle AI-driven insights and complex data relationships—all within PostgreSQL! No need to jump between multiple platforms. PostgreSQL has become a one-stop shop for modern applications, with the scalability and flexibility to drive AI innovations. 🚀 Are you ready to supercharge your data and AI workflows? PostgreSQL with Apache AGE and pgvector could be the key to unlocking your next breakthrough! 🔓✨ #PostgreSQL #ApacheAGE #pgvector #AI #MachineLearning #GraphDatabase #Innovation #DataScience #TechTrends #AIRevolution #DataInnovation
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#30daysofcodechallenge #day26 In MongoDB I learn Grouping and Aggregations($sum,$avg,$max,$min) with grouping and aggregation syntax:- db.<collectionName>.aggregate([{$group: {_id: '$<fieldName>', AnyName: {operator: '$<fieldName>'}}}]) with out grouping and aggregation syntax:- db.<collectionName>.aggregate([{$group: {_id: null, AnyName: {operator: '$<fieldName>'}}}]) #NxtWave #ccbp #nxtwaveccbp #consistency #dailycoding #codingchallege #Mongodb #mongodb #nosql #NoSql #DocumentDatabase
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🟢 Do you work with large datasets and complex queries involving similarity searches for your PostgreSQL database? pgvector might be the missing link required for enhancing your application’s capabilities with AI. Join Semab Tariq, our in-house PostgreSQL Database Developer, on 8 May 2024 for an information-packed session on “pgvector: How to transform your search capabilities with AI”. We will share everything you need to maximize the performance of your queries and lots more. 📅 May 8, 2024 | 3:00 PM GMT ▶ Make sure you have saved your spot: https://hubs.ly/Q02tTz790 ... #PostgreSQL #OpenSource #Database #pgvector #AI #artificialintelligence
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