Irakli Natsvlishvili

Curious about how int4 quantization works in Lucene and how it lines up? Find out in this blog: https://gag.gl/6rreas #ElasticSearchLabs #int4 #Lucene

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Oracle today announced the general availability of #HeatWave #GenAI, which includes the industry’s first in-database large language models (LLMs), an automated in-database vector store!!! #HeatWave #GenAI is available immediately in all Oracle Cloud regions, Oracle Cloud Infrastructure (#OCI) Dedicated Region, and across clouds at no extra cost to #HeatWave customers. #mysql #mysqlheatwave #datalake #oci #oracle #oraclecloud #MDS

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There’s no AI without data. Join us at #DatabaseWorld and learn how to kickstart and expand your #AI initiatives with our Database, Autonomous Database, and Exadata. https://lnkd.in/gXnQhKRH

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Interested in learning more about the new partial flush feature and essential server configuration changes in #Aerospike 7.1? Learn more in this video as Director of Product Management Ronen Botzer walks through optimizing performance for low cost network attached block storage. #Developer #Database

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Oracle today announced the general availability of HeatWave #GenAI, including in-database LLMs, an automated in-database vector store, and the ability to have human-like contextual conversations in natural language. https://lnkd.in/gK-Q44gc

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This Oracle #HeatWave #GenAI demo will show you how you can have contextual conversations in natural language informed by your unstructured content. https://lnkd.in/gJbAXEND

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Great blog post written by Felix Wu specifically addressing SaaS - multi-tenant applications. Maintaining secure data segregation can be difficult when administering a centralised PostgreSQL® database for several clients in the context of multi-tenant Software-as-a-Service (SaaS) applications. Even if a single database can save infrastructure costs, it becomes important to make sure that every organisation has separate access to and control over its data. In order to break through these constraints and enable safe and effective multi-tenant analytics for your PostgreSQL® deployments, this blog article presents a revolutionary solution.

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Discover how scalar quantization can be used to reduce the memory footprint of vector embeddings in Elasticsearch: https://gag.gl/tVVrxQ #ElasticSearchLabs #ML #Elasticsearch

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In my latest blog post, Brent Eager (from StormForge) and I talk about how they use Amazon Managed Service for Prometheus to manage their Optimize Live solution. https://lnkd.in/gFXUzX9n

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🛠️ Save on Scaling Costs! Dynamic scaling in #AzureCosmosDB offers flexible region and partition autoscaling for more cost-effective global operations. 🔗 https://lnkd.in/eT8fjDHD

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Another awesome thing in our upcoming Server 2025 release: the ability to live migrate virtualized AI workloads (link to our updated virtualized GPU blog post: https://lnkd.in/gkjJSnT4)

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Announcing the rename of the Cloud HPC Toolkit to Cluster Toolkit, recent launches, and how to get started building, modifying, and managing clusters for your HPC and AI/ML workloads today

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The expensive cross-AZ traffic fees on AWS are essentially a flexible tool prepared for its sales. It can gain competitiveness by adjusting its discounts when competing with third-party vendors. Now, AutoMQ has helped Kafka break this shackle. #kafka #streaming #automq

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Making AI simple. The meaning and context of every word and everything can be expressed as a vector. These vectors can be 1 click generated, searched for proximity to get answers in languages we talk. The already generated open source vector sets can be imported. Not to be missed talk by Edward & Nipun.

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Learn best practices for transitioning a graph workload to Azure Cosmos DB for NoSQL, based on real-world experience. Key strategies include efficient data modeling and migration to optimize performance and scalability. https://lnkd.in/e4jER4fW

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Cheaper, Faster *and* Fully Managed ! Sounds too good to be true ? Check out how AlloyDB for PostgreSQL delivers 2x price/performance and 4x performance compared to Self-managed PostgreSQL. #alloydb #gcp #postgresql

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Incredibly proud of the latest Kinesis Client Library version (3.0) which, when compared to previous KCL versions, reduces compute costs by up to 33%. KCL 3.0 introduces an enhanced load balancing algorithm that continuously monitors resource utilization of the stream processing workers and automatically redistributes the load from over-utilized workers to other underutilized workers. This ensures even CPU utilization across workers and removes the need to over-provision the stream processing compute workers which reduces cost. Additionally, KCL 3.0 is built with the AWS SDK for Java 2.x for improved performance and security features, fully removing the dependency on the AWS SDK for Java 1.x. #Kinesis #DataStreaming #KCL

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