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Azure Databricks power bi connect disadvantages (in the context of Azure Lane). Hope fabric can solve these problems.

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Databricks is awesome for dealing huge datasets, but Power BI? Not so much. Its in-memory processing kills on huge volume data, and transferring that data takes forever, mostly because of the V-Net gateway. The V-Net gateway is a bottleneck. It kills data transmission and limits you to 30 queries at once. 31st query will be in a queue. If too many queries hit at the same time, you’re stuck with locking issues or mashup errors. Data throughput is another pain point via Vnet gateway. Microsoft’s is working on fixing the Samba JDBC driver at the V-Net gateway, (it’s a dream Microsoft will never fix until all projects moved to Fabric and kick out databricks) Databricks uses a Partner Connect driver for Power BI, using on a Microsoft SPN account. Problem is, anyone without Databricks access can create the reports and see the data—no real user-level security. There’s a big gap in setting up proper governance for this integration. Starting up a SQL warehouse in Databricks is a slow—takes like...
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forbes new about vector database

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  🧠 What’s All the Buzz About Vector Databases? Databases have come a long way—from biblical record-keeping to powerful digital systems. But today, we’re at a major turning point, especially with the rise of generative AI . That’s where vector databases step in. 🚀 Why Vector Databases Matter Vector databases are designed to handle high-dimensional data , like text, images, audio, and video—not just numbers and tables. They’re powered by AI and are great at: 🔍 Similarity search – Finding things that are meaningfully related (like matching a picture with a similar one). 🚨 Anomaly detection – Spotting when something unusual happens in a system. 🕒 Time-sensitive analysis – Understanding not just what happened, but when it happened. These databases use vectors —smart data objects that carry rich, multidimensional information like time, context, and relevance. That makes them perfect for powering tools like AI assistants, recommendation engines, or even monito...
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Location: London, UK
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🔍 Vector Database with Pinecone: The Future of AI Search

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  🔍 Vector Database with Pinecone: The Future of AI Search In today’s AI-driven world, traditional databases are struggling to keep up with the need for semantic search —that is, understanding the meaning behind data rather than just matching exact terms. This is where vector databases come into play, and Pinecone is one of the leaders making it accessible and powerful. 🤖 What is a Vector Database? Unlike standard databases that store rows and columns, a vector database stores embeddings —numerical representations of data like text, images, or audio. These embeddings allow machines to "understand" similarity in a more human way. For example, in a vector database: "car" and "vehicle" are close. "apple" (the fruit) and "banana" are close. "apple" (the company) and "banana" are not close. This semantic similarity is essential for AI tasks like: Chatbots Recommendation engines Document search ...
Location: London, UK
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How to Choose the Right Vector Database in 2025

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  How to Choose the Right Vector Database in 2025 As the demand for artificial intelligence (AI), large language models (LLMs), and semantic search grows, vector databases have emerged as a foundational technology. Whether you're building a chatbot, recommendation engine, or intelligent search system, selecting the right vector database can make or break your project. But with so many options—like Pinecone, Weaviate, Milvus, Qdrant, and Chroma— how do you choose the best one for your needs? In this guide, we’ll break down the key criteria to consider when choosing a vector database, helping you find a solution that balances performance, scalability, and cost. ✅ Key Factors to Consider When Choosing a Vector Database Choosing a vector database isn’t a one-size-fits-all decision. It depends heavily on your use case , scale , and budget . Here’s what to look for: 1. Scalability: Can It Grow with Your Data? AI applications often process millions—even billions—of vectors . You n...
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Location: London, UK
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The Role of AI and Machine Learning in Vector Databases

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The Role of AI and Machine Learning in Vector Databases In the age of data-driven decisions and artificial intelligence, traditional databases are no longer sufficient to meet modern demands. As applications become more intelligent and user expectations grow, the need for real-time, context-aware data retrieval has become critical. Enter vector databases —a new kind of data storage system designed to work seamlessly with AI and machine learning (ML) models. This article explores how vector databases are reshaping industries through recommendation engines, content discovery, and generative AI applications. We'll also examine why they’re essential for the future of data architecture. What Are Vector Databases? Before diving into their AI applications, let’s define what vector databases are. A vector database stores data as high-dimensional vectors , which are numerical representations of complex data types like text, images, audio, and more. These vectors capture semantic mea...
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Location: London, UK
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🔝 The 5 Best Vector Databases in 2025

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  🔝 The 5 Best Vector Databases in 2025 1. Pinecone Best For: Production-scale AI applications and LLMs Overview: Pinecone remains a market leader in 2025 due to its fully managed, cloud-native architecture . It’s widely adopted for powering retrieval-augmented generation (RAG) , real-time recommendation systems, and semantic search. Key Features: Scalable and serverless infrastructure Real-time indexing and filtering Low-latency ANN (Approximate Nearest Neighbor) search Tight integration with OpenAI and LangChain Use Cases: Chatbots using LLMs AI-powered customer support Personalized product recommendations Pros: No infrastructure management Super fast response times Native vector filtering with metadata SEO Keywords: pinecone vector database, best AI database 2025 2. Weaviate Best For: Open-source developers and enterprise AI search Overview: Weaviate is a powerful open-source vector database that supports hybrid search (vect...
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Location: London, UK
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