[Part-2] Comprehensive Technical Guide: Harnessing the Power of Elasticsearch and Node.js Integration🚀🔍 Welcome back to our series on integrating Elasticsearch with Node.js. If you missed Part-1, you can catch up here : https://lnkd.in/e-7ws4at , where we introduced the foundational concepts and set up our development environment. In this comprehensive guide, we will take you on a technical deep dive into the seamless integration of Elasticsearch with Node.js. You'll learn how to set up and configure both technologies, execute powerful search queries, and optimize performance for your specific use cases. Whether you're building a search engine, an analytics dashboard, or any data-driven application, this guide will equip you with the knowledge and tools needed to harness their collective power, propelling your development projects to new heights.🌟💡 #Elasticsearch #NodeJS #Integration #DataAnalytics #FullStackDevelopment #TechGuide #SearchEngine #DataScience
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In our latest article, Humda Zainab explains how Elastic Builder in Node.js can make your Elasticsearch experience more efficient. Link to the article: https://lnkd.in/dkfBpbiN #ElasticSearch #Nodejs
Elastic Builder in Node.js: Simplifying Elasticsearch Query Creation
medium.com
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Curious about how OpenObserve compares to Elasticsearch? 🤔 - OpenObserve is specialized log search engine, unlike Elasticsearch which is a a general search engine. - OpenObserve stores un-indexed data in compressed format, reducing costs (~140x less than Elasticsearch) - Uber's experience showed that aggregation queries are faster with OpenObserve's columnar data storage - Built-in GUI, no need for additional components like Kibana. - Hassle-free cluster management, no index mapping required. Read more here: https://lnkd.in/gBwuc5uK #OpenObserve #Elasticsearch #LogSearch #DataStorage
OpenObserve: Introduction
openobserve.ai
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Curious about how OpenObserve compares to Elasticsearch? 🤔 - OpenObserve is specialized log search engine, unlike Elasticsearch which is a a general search engine. - OpenObserve stores un-indexed data in compressed format, reducing costs (~140x less than Elasticsearch) - Uber's experience showed that aggregation queries are faster with OpenObserve's columnar data storage - Built-in GUI, no need for additional components like Kibana. - Hassle-free cluster management, no index mapping required. Read more here: https://lnkd.in/gBwuc5uK #OpenObserve #Elasticsearch #LogSearch #DataStorage #clustermanagement
OpenObserve: Introduction
openobserve.ai
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Curious about how OpenObserve compares to Elasticsearch? 🤔 - OpenObserve is specialized log search engine, unlike Elasticsearch which is a a general search engine. - OpenObserve stores un-indexed data in compressed format, reducing costs (~140x less than Elasticsearch) - Uber's experience showed that aggregation queries are faster with OpenObserve's columnar data storage - Built-in GUI, no need for additional components like Kibana. - Hassle-free cluster management, no index mapping required. Read more here: https://lnkd.in/gBwuc5uK #OpenObserve #Elasticsearch #LogSearch #DataStorage #fintech
OpenObserve: Introduction
openobserve.ai
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Sr. AI Architect - AI LAB for ISV at Microsoft. Book Author. International Keynote Speaker. AI Coach for ISV & Startups.
Discover the new RAG-on-PostgreSQL solution for easy querying of structured data. Includes a FastAPI backend, React frontend, and deployment to Azure Container Apps. https://lnkd.in/d3pxrzsg #RAGonPostgreSQL #StructuredData #FastAPI #React #Azure #DataQuerySimplified
RAG on structured data with PostgreSQL
techcommunity.microsoft.com
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In the last few months I have completely revised the code generator, which is used to generate large parts of the Elasticsearch 8.x .NET client from an API specification. As a result, it does now support over 200 new endpoints, all aggregations and many other features that were not available before. As a consequence of reaching support for almost all features of #Elasticsearch 8 with client version 8.13.x we have deprecated NEST (7.17.x). NEST will reach end of life at the end of the year. Read more about the evolution of the .NET client in my related blog post Elastic:
Evolution of the Elasticsearch .NET Client — Elastic Search Labs
elastic.co
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Now you can write natural language queries to get results from Compass and even create visualisations in MongoDB Charts. Want to find out what else was announced at #MongoDBlocal London? Catch up on the buzz with our latest blog, featuring all the exciting MongoDB announcements you might have missed!
JUST IN! Recap of Product Announcements at MongoDB.local London 2023
mongodb.com
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Developers, rejoice! 🎉 Amazon DocumentDB (with MongoDB compatibility) just introduced vector search, adding new dimension to your database. Now, you can search based on meaning or semantics, not just keywords! I invite you to explore this feature through this blog, which also has link to code samples. #aws #AWSreInvent! #AmazonDocumentDB #amazonbedrock #amazonsagemaker #vectordatabase #semanticsearch
Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available | Amazon Web Services
aws.amazon.com
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🤖 For folks getting deep into GenAI 🤖 While GenAI and LLMs provide the potential for more granular insights, improved user experiences, and a variety of use cases – they can also lead to architectural sprawl and complexity. Find out more about how developers leverage GenAI without needing to learn a suite of new tools or proprietary services with MongoDB Atlas! https://lnkd.in/gpH7SxWS #GenAI #LLM #MongoDB
How to Avoid GenAI Sprawl and Complexity | MongoDB Blog
mongodb.com
