Kinetica

Kinetica is excited to be a core partner in the groundbreaking Dell Technologies AI for Telecom solution. As the real-time retrieval engine within the stack, Kinetica’s unique ability to handle vector search and ad hoc SQL queries —at speed/scale— unlocks the potential of GenAI for all of your data —tabular and text. Powered by NVIDIA technology and deployed on Dell hardware, this collaboration exemplifies high-performance AI infrastructure tailored for the telecom industry.

Imagine being able to speak into your phone to get an immediate, analytically detailed update on the current state of your cell tower network or supply chain. Kinetica, the real-time database for generative retrieval and analytics, makes this a reality. Join Co-founder and CEO Nima Negahban for a tutorial on building GenAI application that retrieves insights from both text AND tabular data, delivering more contextually accurate answers for your enterprise in real time. ⏰ Thursday September 26, 2024 at 13:00 ET https://lnkd.in/eqTyNRua #vectordb #vectorsearch #rag #Text2SQL #NvidiaNemo #cuvs #realtimeanalytics #shiftleft

Updating benchmarks or running real-time generative AI applications, requires access to the latest GPU hardware like the NVIDIA GH200 Grace Hopper. Leveraging our partners at Vultr, a modern cloud platform, and cutting edge GPU hardware, Kinetica delivers the same performance as platforms like Databricks – at half the cost. More to come on this soon... 👀

Kinetica is proud to be part of the NVIDIA RAPIDS cuVS ecosystem. NVIDIA integrates cuVS into vector search and database technologies to deliver unparalleled speed and efficiency in information retrieval. Building on low-level CUDA primitives, the RAPIDS cuVS library contains optimized algorithms for approximate nearest neighbors and clustering, along with many other essential tools for accelerated vector search. https://rapids.ai/cuvs/ #Vectorsearch #Vectordatabase #RAG #RAPIDS #cuVS

Kinetica leads the popular #VectorDBBench benchmark - capable of ingesting vector embeddings 15X faster than the next best vendor (CPU only)! Integrations with NVIDIA RAPIDS RAFT as well as RAFT’s market leading algorithm for approximate nearest neighbor (ANN) search, CAGRA, allows us to harness the power of the GPU - promising even better performance! “At Kinetica, our focus has always been on delivering real-time insights in a rapidly evolving data landscape through our natively vectorized GPU optimized architecture. The introduction of real-time vector similarity search perfectly aligns with our technology foundation and underscores our position at the forefront of data-driven innovation.” ” - Nima Negahban, Cofounder and CEO, Kinetica. *All competitive benchmark results taken from official leaderboard entries not from Kinetica testing

Confluent is celebrating one year of the "Connect with Confluent" (CwC) program! 🎉 As one of the partners in the category "Date Warehouse/Lake" we look forward to many more years of helping our customers extract value from their #DatainMotion Learn more about the program here: https://lnkd.in/eTKmaFCP #RealtimeAnalytics #Confluent #Kinetica

According to MIT's Peter Weill, companies that operate in real-time have achieved 97% higher profits and 62% higher growth than their industry competitors. Kinetica's database with our ability to simultaneously ingest and analyze massive data streams with low latency is the easiest (and most cost effective) way bridge the real-time gap! Full article here: https://lnkd.in/eu72ErBj

In this tutorial, Hari Subhash demonstrates how to create a 1.2m node graph network - while running computational expensive analytical queries every 5 seconds using just an XS CPU only instance of Kinetica. All using SQL, relational tables, and without complex data engineering (or the need for a separate graph database). 👉 https://lnkd.in/eqxKZ_D9 #graphdatabase #graphdatascience #graphsolvers #shortestpath #kafka

Leverage all your data & reduce hallucinations without sacrificing performance. 😎 #vectorsearch #rag #LLM #CAGRA #gpudb

Vector databases powered the first wave of GenAI. But new applications requiring the integration of real-time and tabular data are beginning to highlight their limitations. Click to explore the future of real-time, multimodal retrieval engines that are going beyond just #vectorsearch. https://lnkd.in/eV_iJZkT