Qarbine’s Post

Just learnt about a cool MongoDB's aggregation pipeline with the $facet stage. This powerful feature allows for parallel execution of multiple sub-pipelines, enhancing efficiency in complex data transformations. Consider the attachment #MongoDB #AggregationPipeline #DataTransformation

One thing we think a lot about at Unstructured is how preprocessing can drive better RAG results. In addition to rendering documents into JSON, our file transformation pipelines generate 30+ categories of metadata that we attach to each specific document element. However, to realize the value of this metadata during RAG retrieval, you have to be able to index it. Crucially, MongoDB Atlas vector search supports metadata filtering during the retrieval step of RAG. It’s widely known that the efficacy of nearest neighbor search diminishes after 100M+ vectors, but by combining metadata filtering with hybrid search, organizations can quickly and efficiently search across billions of records. Our new enterprise Platform, launching in February will have connectors to MongoDB ready at launch. Sign up here for early access: https://lnkd.in/eYdg_9Cv

Are you in NYC on May 2nd, 2024? MongoDB.local event is happening at the Javits Center with breakout sessions, hands-on labs, keynote, and ask the experts. If you are there, come by the 1:15 session being held in the Lightening Zone #B to lean about AI Driven Data Discovery and Contextualization for Industrial Unified Namespace. Humza Akhtar, PhD from MongoDB and I from BigID will be covering how to use BigID and MongoDB to drive unified namespace. Now, I know what you are thinking, this may not apply to me as I in not in manufacturing. I can assure you, data discovery, contextualization and unifying data can be applicable to many different use cases including customer, product, vendor and other types of entities to connect the dots across disparate data sets. How many companies are trying to create a customer 360 view only to get to 120 degree view? This session will cover how to correlate data and build entities (connect the dots across an object from disparate data sources to enrich data). Abstract for the session For manufacturing organizations, accessing the full potential of their data is of paramount importance. Although there has been significant investment in Industry 4.0, it is now well understood that starting on such digital transformation projects is not easy and scaling it up is hard. Unified Namespace is a novel idea that proposes an always-updating, contextualized repository of data and information for all manufacturing assets. It is a powerful event-driven architecture that allows for seamless communication between assets in a manufacturing environment and seamless communication and data sharing between assets to help in scalability of Industry 4.0 initiatives. In this session, learn the importance of standardizing data across the manufacturing floor, and how BigID and MongoDB together provide the essential AI driven data exploration, classification, contextualization and flexible storage tools to realize the vision of Unified Namespace. Hope to see you there. https://lnkd.in/gJ3TV4pA

🥑 MongoDB meetup in #bengaluru is here! Do RSVP🥑 I've been waiting since months to go to a MongoDB meetup, learn and deep dive into the tool. I've working with MongoDB from some 3 years now and absolutely love its community and documentation! Let's catch-up soon here and talk more! ⭐ #mongodb #mongoose #js #devrel #developer #meetup

🚀 A Week of Growth and Innovation at MongoDB! 🚀 This week was all about GenAI and expanding knowledge: - Successfully demonstrated a cutting-edge GenAI integration with MongoDB, showcasing the power of our platform in enabling AI-driven applications. - My weekend read is here: "Mastering MongoDB 7.0" - can't wait to dive into the latest features and best practices! Here's to pushing boundaries and making data work smarter! #MongoDB #GenAI #Innovation #ContinuousLearning

I’m happy to share that I’ve obtained a new certification: MongoDB Aggregation from MongoDB! 😀 Unlock the power of MongoDB Aggregation Framework to process and transform your data efficiently! 🔄 Whether it's filtering, grouping, or calculating stats, this powerful tool is your go-to for working with complex data sets. 📊💡 Key features include: $match: Filter your documents ⚙️ $group: Group & summarize 🔢 $project: Reshape data views 🔄 $sort: Organize results by fields 🔍 Take your MongoDB skills to the next level! 🌐 #MongoDB #DataScience #AggregationFramework #TechSkills #DataProcessing

Day 19 of the #25DaysLearningChallenge 🚀 Today, I tackled the first step of CRUD operations: Creating Documents. MongoDB’s flexible, schema-less nature allows you to insert data without predefined structures, which makes development fast and dynamic! 🔍 Key Takeaways: ➤ Insert Documents: ➔ insertOne() – Insert a single document into a collection. ➔ insertMany() – Insert multiple documents at once. ➤ MongoDB’s flexibility allows different document structures in the same collection. ➤ Explored MongoDB's built-in data types like strings, numbers, arrays, and embedded documents. Masai | Prepleaf by Masai #DailyLearning #WebDevelopment #MongoDB #Database

I used Neo4j and MongoDB both in one of my personal project. Many interviewers asked me why I used MongoDB when Neo4j supports geospatial indexing too. Here are the reasons: 1. Performance: MongoDB generally outperforms Neo4j in spatial query processing, especially as the dataset grows larger. Neo4j can become slow with high node counts. 2. Scalability: MongoDB's spatial indexing scales better and handles larger datasets more efficiently compared to Neo4j. 3. Functionality: Neo4j offers advanced capabilities for complex spatial queries due to its graph structure, while MongoDB provides faster and more straightforward geospatial queries.

🌟 Primary & Secondary Sharding Nodes Unleashed! 🚀 MongoDB just stepped up its sharding game with the introduction of primary and secondary nodes. This dynamic duo is set to revolutionize the way we handle data distribution and replication in our MongoDB clusters. 🔗 Key Benefits: ✨ Enhanced Performance: Distribute read and write operations across primary and secondary nodes, optimizing response times and ensuring faster data access. ✨ Improved Fault Tolerance: With primary and secondary nodes in play, your system gains robust fault tolerance. If a primary node goes down, the secondary node seamlessly takes over, minimizing downtime. ✨ Scalability on Steroids: Achieve unprecedented scalability by horizontally adding secondary nodes. Watch your MongoDB cluster grow effortlessly as your data demands increase. 🛠️ How It Works: ✨ Primary Node: The primary node is your data's superhero, responsible for handling write operations and coordinating data distribution across the sharded cluster. ✨ Secondary Nodes: These nodes act as trusty sidekicks, replicating data from the primary node. They're ready to step in if the primary node needs a break or encounters any issues. #MongoDB #Sharding #DatabaseScaling #TechInnovation #DataManagement #webdevelopment #softwaredevelopment

📢 Webinar: Building Advanced RAG Apps with MongoDB, Unstructured, and LangGraph 🗓 September 26, 2024 | 12 PM ET 🫡 It’s time to go beyond POCs and naive RAG. Start by improving retrieval performance with metadata pre-filters. What you'll learn: 🚀Process raw documents with Unstructured's Serverless API: chunking, embedding, and extracting useful metadata 🚀Combine metadata with MongoDB Atlas Vector Search for better retrieval and relevance 🚀Orchestrate RAG systems with a self-querying retriever using LangGraph Speakers: Apoorva Joshi, MongoDB Maria Khalusova, unstructured.io Walk away with actionable techniques you can apply right away: https://lnkd.in/eADVpDX9