Kinetica ’s Post

Kinetica Engineering leaders Bilge Kaan Karamete & Eli Glaser recently published research on how to generate general graph node embeddings from knowledge graph representations. "Mapping of graphs to vectors has a distinct advantage that they can be applied in a standard manner using simple vector functions in many #AI modules. The alternative of using #knowledgegraph analytics has almost no standardization in many downstream AI applications provided by various #graphdatabase vendors." 👀 Link here: https://lnkd.in/gpfkyEpC

Kinetica is featured alongside Cohesity DataStax and NetApp as a leading NVIDIA Data Platform Partner working with their NeMo Retriever NIM microservices to boost AI models’ accuracy and throughput. "Kinetica will use NVIDIA NeMo Retriever to develop LLM agents that can interact with complex networks in natural language to respond more quickly to outages or breaches — turning insights into immediate action." This integration allows our users to invoke embedding and inferencing models provided by NIM directly within Kinetica, simplifying the development of production-ready generative AI applications that can converse with and extract insights from enterprise data. By combining these services with Kinetica’s robust compute and vector search capabilities, developers can easily build data copilots that meet rigorous performance and security requirements. Check out this tutorial to see how to connect Kinetica to #NIM with just a few SQL statements: https://lnkd.in/egXQrYwj

For the last 40 years databases have been designed with one thing in mind: -To treat compute as scarce resource 😨 What happens when you design a #database for a future where compute is an abundant resource?

#genai applications for data needs a retrieval engine that can do both vector and analytic operations on real-time data at speed and scale.

Benchmarks are good, but independent benchmarks are better. 😏 Radiant Advisors completed the industry standard TPC-DS benchmark comparing Kinetica against other leading analytical databases...and found that Kinetica outperformed overall! (5x faster than #BigQuery for example) Check out the results of this, and other benchmarks, for yourself: https://lnkd.in/ecccnbp9 #tpcds #realtimeolap #realtimedatabase #gpudatabase

Migrating data from one database to another is a major challenge in enterprise settings, and it’s a pain point many of many of our customers face, when moving to Kinetica. This upcoming webinar hosted by RosettaDB addresses this very issue head-on 👊. Webinar: Automating Schema & Data Migration with RosettaDB and Kinetica Learn how you can leverage RosettaDB, Git and GitHub Actions to effortlessly move data to Kinetica with robust schema management. 📅 Date and Time: Tuesday, July 9th, 1:00 PM EST 🔗 Register here: https://lnkd.in/gEWxRAHV

Hey Rockset users 👋! Kinetica is the ultimate real-time database for complex analytic workloads. Transitioning your data to Kinetica is seamless, requiring just a few lines of SQL and only minutes to set up. Our platform leverages GPU acceleration to handle OLAP, spatial, time series, graph, and vector analytics with unparalleled speed and efficiency. Try us out: https://lnkd.in/eCJ2aX6x

Kinetica has once again been included in this year's DBTA 100! 🥳 Database Trends and Applications publishes an annual list of 100 forward-thinking companies seeking to expand what’s possible with data for their customers. See the full list: https://lnkd.in/gbpMB4Jr #GPUdatabase #DBTA100

Kinetica running on Dell Technologies and NVIDIA hardware provides crucial speed, scale & native integration with edge devices like #Jetson required to bring processing power closer to the data source! 👊 #edgecomputing #edgeai #realtimeanalytics #multicloud