Abhinav Agarwal’s Post

Generative AI with Amazon RDS for SQL Server Vector Data Store: 1. Domain Context Integration: Utilize existing operational data on RDS for SQL Server to provide your generative AI models with domain-specific context. 2. Vector Data Management: Leverage the robustness, scalability, and reliability of SQL Server for efficient vector data management. 3. Optimized Vector Operations: Benefit from built-in optimizations like SIMD and AVX-512 within columnstore indexes for accelerated vector processing. 4. Custom Similarity Metrics: Implement your preferred similarity metric, such as cosine similarity, as a user-defined function within SQL Server for tailored vector searches. 5. Secure and Scalable Data Storage: Trust in the security and scalability of RDS for robust and efficient storage of your vector data. By using SQL Server on RDS as a vector data store, you can unlock the full potential of RAG-based generative AI with familiar tools and a reliable infrastructure. This approach enables enhanced performance of generative models, efficient vector data management with industry-standard SQL queries, and customizability through user-defined functions for vector searches. #AIArchitect #MachineLearningExpert #GenerativeAI #VectorEmbeddings #AmazonRDS

🚀 🚀 Revolutionizing Data Processing and unlocking AI Efficiency with our #EcosystemSQLELT. Processing vast volumes of structured and unstructured data is crucial for AI-driven data initiatives. Traditional hand-coded solutions can increase costs and restrict scalability. Explore the transformative power of low-code/no-code tools that enable business users to rapidly build ELT pipelines, promoting data democratization and quick insights. Discover how Informatica® SQL ELT can optimize your cloud data strategies, reduce costs, and improve performance with modern cloud data architecture technologies.  🔗 Click here to get started at no cost with #CloudDataIntegration : https://lnkd.in/gMP_fiUW ⚡ ⚡ Pratik Parekh Amol Dongre Makesh Renganathan Siddharth Mishra Alok Manjrekar Rajat Kumar Pandey Mohammad Salim Zia Preetam Kumar Hemalatha Narendrababu 💡 #CDI #SQLELT #GenAI #LLM #Snowflake #Databricks #Fabrics #GoogleBigQuery #AWSRedshift

Hear from Databricks customers about their experiences using the Data Intelligence Platform. From ETL to data warehousing to #Generative AI, see what our customers have to say about simplicity and unification for their data, AI and governance use cases. Check out the reviews!⭐️

We're excited to announce the launch of our latest feature at Genie AI. With this new feature, you can seamlessly integrate all your data sources—whether relational databases, NoSQL databases, SaaS tools, or APIs—into a single, unified data source. All your data together, at last...With Genie AI, you can query your data sources as a single, cohesive entity right out of the gate, access data directly from its source in real-time, and seamlessly blend and cache data from multiple sources. Plus, our platform allows for streaming ingestion from platforms like Kafka and Segment, instant replication with CDC, and AI-powered SQL generation, eliminating the need for tedious data preparation and transformation processes. Upgrade to Genie AI today to revolutionize your data integration process and unlock unparalleled insights!  #GenieAI #DataIntegration #RealTimeInsights #AI #BusinessIntelligence

Quite a few announcements about Bedrock today to simplify the life of Generative AI builders: Bedrock prompt caching Bedrock intelligent prompt routing Bedrock Kendra generative AI index Bedrock knowledge bases structured data retieval Bedrock knowledge basew support for GraphRAG Bedrock data automation #bedrock #aws #reinvent #genai

As #AI and #GenAI projects surge, the demand for vast amounts of data has become critical. The VAST Data Platform stores, catalogs, enriches and secures structured and unstructured data for data-intensive computing, deep learning and AI workloads. The platform is built on a distributed systems architecture called DASE (Disaggregated and Shared-Everything) and includes VAST DataStore for unstructured data management, VAST DataSpace for edge-to-cloud data access, VAST DataBase for structured data management, and the VAST DataEngine (available this year) for providing actionable data insights. This platform solidified VAST as one of the coolest #BigData management and integration tool companies of CRN’s 2024 Big Data 100!

#OmniAI Transforms Business Data for #AI 🌐🤖 OmniAI helps #businesses in regulated industries like healthcare and finance with data analytics 📊 Their #tools convert unstructured data, like sales calls and Slack messages, into #usable formats for AI. OmniAI syncs with data storage #services (e.g., Snowflake, MongoDB), preps the #data, and runs chosen models 🔄

🤠 Wrangling petabytes of unstructured data and metadata for #AI is a huge IT challenge! We're here to help. The backbone of every storage system is its #metadata database and efficiently managing #UnstructuredData is business-critical. 💡 The powerful integration of GRAU DATA GmbH MetadataHub + #ScalityRING empowers IT teams and developers to handle massive volumes of unstructured data, making it AI-ready. Transform how unstructured data and its associated metadata are managed to make it actionable with: ✳️ faster data access, ✳️ more visibility, ✳️ unlocked AI-ready workflows, ✳️ and improved AI outcomes. *************** Get the details in our blog: https://bit.ly/40t6JbI ************** P.S. Scaling metadata is one of the #10DimensionsOfScale that every storage architecture should be required to handle. You can learn more about #MultidimensionalScaling and #RedefiningScale for AI in the blog.

The convergence of data engineering and AI is fundamentally reshaping our technical landscape, driven by the architectural demands of modern AI systems. At the core of this transformation is the rise of retrieval-augmented generation (RAG) architectures and vector databases, which require a deep understanding of both data infrastructure and AI models. This fusion isn't just a trend, it's a technical necessity as we build systems that must seamlessly handle both traditional data pipelines and AI-specific requirements like embedding spaces, semantic similarity calculations, and real-time vector search capabilities. The technical backbone of this convergence manifests in hybrid architectures that combine traditional data engineering with AI-specific components. Consider a modern RAG system: it requires expertise in document processing frameworks (like LangChain or LlamaIndex), vector stores (such as Chroma, Pinecone, or Weaviate), and embedding models (like all-MiniLM-L6-v2 or OpenAI's text-embedding-3-large). The system must handle traditional ETL challenges while also managing AI-specific concerns such as token windows, chunking strategies, and embedding consistency, demanding professionals who understand both domains deeply. This conjunction points toward an "AI-First Data Architecture" paradigm where data infrastructure is designed with AI capabilities as a primary consideration. Key technical challenges include managing embedding consistency in streaming updates, handling semantic drift in production language models, and optimizing for both traditional SQL queries and vector similarity searches. Success in this new landscape requires understanding concepts from both worlds: from database sharding to transformer architectures, from data warehousing to prompt engineering. The most valuable professionals will be those who can architect systems that elegantly bridge these traditionally separate domains. #GenerativeAI #AI #AIValue #LeadingAIForTheFuture #DataEngineering #MachineLearning #RAG #TechnicalLeadership #AITransformation

AI is only as powerful as the data infrastructure behind it. Integrating AI into enterprise applications is no small feat. From siloed vector databases to scalability challenges, organizations face steep hurdles in deploying AI efficiently. With EDB Postgres AI, powered by pgvector, you can: 🔹Simplify AI infrastructure by consolidating vector databases, reducing disk footprint by 5X and powering 18X cost efficiency with object storage 🔹Achieve 4.22X faster query performance to support complex operations for advanced AI applications 🔹Enable real-time indexing and querying for efficient vector data operations, supporting hybrid search on various LLM embeddings and consistent handling of mixed workloads Explore our guide to see how you can transform your AI strategy with Postgres ➡️ https://bit.ly/3ZyTPb3 #EDBPostgresAI #JustSolveITWithPostgres #pgvector #AIPlatform #DataInfrastructure #PostgreSQL #AISolutions #EnterpriseAI