MongoDB User Group Pune’s Post

Bigtable, Google Cloud's NoSQL database that powers services like Search and YouTube, now supports GoogleSQL. This means you can use familiar SQL syntax to build applications for AI, fraud detection, and more, leveraging Bigtable's speed and scalability. #googlecloud #bigtable #nosql #sql #database #developers #announcement #this_post_was_generated_with_ai_assistance #responsibleai https://lnkd.in/ea-xZ6vD

Another set of fantastic of innovations from the team. Azure AI Extension GA https://lnkd.in/g_Mv64EM Automated Index Tuning Preview https://lnkd.in/gM8NGyqj In Database Embedding Generation Preview https://lnkd.in/g4ZVn3RH #msbuild #azure #azurepostgres #postgresql #genai #autonomousdatabases

Are you interested to know, how your developers using Amazon Q Developer? Sharing latest #AWS #Blog published by my friend David Ernst and @Joseph Miller, which provides solution to leverage Amazon Q Developer’s logs from the Integrated Development Environment (IDE) and terminal, captured in AWS CloudTrail. The logs will be queried directly using Amazon Athena from Amazon S3 to analyze feature usage, such as in-line code suggestions, chat interactions, and security scanning events. Check out this blog - https://lnkd.in/dgri6yek #aws #amazon #amazonqdeveloper #genai #ngde #productivity #terraform #hashicorp #ai #ml

Incorporate semantic search with embedded vectors in Azure Database for Postgres. How to enhance user experience by refining search results based on natural language inputs. Watch here. Watch the full video here: https://lnkd.in/gmgpHj_s VIDEO SYNOPSIS: Use Postgres as a managed service on Azure. As you build generative AI apps, explore advantages of Azure Database for Postgres Flexible Server such as integration with Azure AI services, as well as extensibility and compatibility, integrated enterprise capabilities to protect data, and controls for managing business continuity. #PostgreSQL #OpenSource #Azure #Database #PostgresOnAzure

Relational databases have served us well, but modern demands expose their limits. Flexibility, scalability, and AI integration? Tough! 🤯 Enter MongoDB Atlas – a flexible, intuitive, cloud-based database for today's developers. Learn why and how to switch. 👇 https://lnkd.in/ghqRMFy2 #DataModernization #GoogleCloud #MongoDB

The new data zone model for EU and US makes it much easier for organisations within these geographies to use #Azure #OpenAI at scale.

🚀 Exciting news from Microsoft Ignite! MongoDB and Microsoft are expanding their powerful tools to help customers unlock the full potential of their data. ❗ The consensus? MongoDB isn't just playing in the AI space; it's rewriting the rules. Investors are eyeing MongoDB as one of the hottest AI bets on the market right now. 1️⃣ Enhance AI with Enterprise Data: MongoDB Atlas is now integrated with Azure AI Foundry and Azure OpenAI Service, enabling businesses to build RAG-powered applications like chatbots and copilots using proprietary data—all without extra coding. 2️⃣ Real-Time Insights: Open Mirroring in Microsoft Fabric ensures seamless, near real-time data sync between MongoDB Atlas and OneLake, powering advanced analytics, AI predictions, and BI reports. 3️⃣ Flexible Deployment: MongoDB EA is now available on Azure Marketplace for Azure Arc-enabled Kubernetes, allowing robust, scalable deployments across on-premises, multi-cloud, and hybrid environments. These innovations are already empowering companies like Trimble Inc. and Eliassen Group to accelerate AI-driven solutions. Together, MongoDB and Microsoft are delivering unmatched flexibility for building modern, data-rich applications! #MongoDB #Microsoft #Azure #AI #DataInnovation #MongoDBAtlas #VectorSearch #RAG

🌟 Excited to share the latest from our blog series on #data #privacy! 🚀 In Part 1, we delved into building a robust pseudonymization service at scale, harnessing the breadth of #aws #managed #services. Part 2 took it up a notch, illustrating seamless integration of batch and streaming workloads with #Amazon #EMR and #AWS #Glue. 💡 But wait, there's more! Dive into our bonus content on GitHub: bit.ly/3Um6Su6, where we unveil an #Amazon #Athena based consumption pattern, unlocking insights on re-identifying data at query time. Rahul Shaurya, Andrea Montanari, María Guerra Pellón, Pushpraj Singh https://lnkd.in/erDhTfne

This post seems to have been taken down, but Amazon Bedrock Knowledge Bases (RAG) appears to support a new vector database, MongoDB! https://lnkd.in/gqbg5GuZ #genai #generativeai #llm #ai #machinelearning #vectordb #mongodb #aws #awscloud #amazon #amazonaws #awscommunity

🔍 **Elastic Search, Kibana, and Data Filtering in AWS and GCP** ☁️ **Elastic Search:** Elasticsearch is a distributed, RESTful search and analytics engine, designed to store, search, and analyze large volumes of data quickly and in near real-time. It's widely used for log and event data analysis, full-text search, and other complex search queries. **Kibana:** Kibana is an open-source data visualization and exploration tool that excels in log and time-series analytics, application monitoring, and operational intelligence use cases. It offers features like histograms, line graphs, pie charts, and heat maps for visualizing large data sets. **Data Filtering in AWS:** 1. **Data Ingestion**: Data is ingested using services like AWS Kinesis or AWS Data Pipeline. 2. **Storage**: Data is stored in Amazon S3 or Amazon Redshift. 3. **Processing**: AWS Glue or AWS Lambda transforms and filters the data. 4. **Indexing**: Processed data is indexed into Amazon Elasticsearch Service. 5. **Visualization**: Kibana, integrated with Amazon Elasticsearch Service, is used for data visualization and analysis. **Data Filtering in GCP:** 1. **Data Ingestion**: Data is ingested using services like Google Pub/Sub or Google Dataflow. 2. **Storage**: Data is stored in Google Cloud Storage or BigQuery. 3. **Processing**: Google Cloud Dataflow or Google Cloud Functions transforms and filters the data. 4. **Indexing**: Processed data is indexed into Elasticsearch on Google Cloud. 5. **Visualization**: Kibana, integrated with Elasticsearch on Google Cloud, is used for data visualization and analysis. Understanding the distinctions and capabilities of AWS and GCP for data filtering and visualization can significantly impact your decision-making process for cloud services. 🌐📊 #CloudComputing #DataAnalytics #Elasticsearch #AWS #GCP #Kibana #BigData #DataVisualization #TechInsights