Researchers at ServiceNow Propose a Machine Learning Approach to Deploy a Retrieval Augmented LLM to Reduce Hallucination and Allow Generalization in a Structured Output Task - MarkTechPost

Large Language Models (LLMs) have made it economically possible to perform tasks involving structured outputs, such as converting natural language into code or SQL. LLMs are also being used to convert natural language into workflows, which are collections of actions with logical connections between them. These workflows increase worker productivity by encapsulating actions that can run automatically under certain circumstances.  Particularly in tasks like producing natural language from prompts, Generative Artificial Intelligence, or GenAI, has demonstrated impressive capabilities. However, one major disadvantage is that it often produces false or absurd outputs, which are called hallucinations. In order to achieve universal

MySQL: Latency and IOPS | Die wunderbare Welt von Isotopp

The MySQL adaptive hash index

The adaptive hash index help to improve performance of the already-fast B-tree lookups

Monitor query performance with Performance Insights on Amazon RDS for SQL Server | AWS Database Blog

Amazon RDS Performance Insights is a powerful feature of Amazon Relational Database Service (Amazon RDS) that provides real-time and historical insights into your database performance. Whether you’re troubleshooting performance bottlenecks, identifying slow queries, or optimizing your system, Performance Insights is able to help. With Performance Insights, you gain a deeper understanding of your database’s behavior. […]

Snapshot too old in YugabyteDB - DEV Community

SQL databases store the current state and enough information to read about a previous state with... Tagged with yugabytedb, snapshot, mvcc, flashback.

Snowflake AI Research Team Unveils Arctic: An Open-Source Enterprise-Grade Large Language Model (LLM) with a Staggering 480B Parameters - MarkTechPost

Snowflake AI Research has launched the Arctic, a cutting-edge open-source large language model (LLM) specifically designed for enterprise AI applications, setting a new standard for cost-effectiveness and accessibility. This model leverages a unique Dense-MoE Hybrid transformer architecture to handle SQL generation, coding, and following instructions efficiently. With its capability to operate under lower computing budgets, Arctic offers a high-performance solution for businesses looking to integrate advanced AI without the substantial costs typically involved. Snowflake Arctic includes two variants:  Arctic Base Arctic Instruct  Both models are hosted on Hugging Face and are readily accessible for use. The Arctic Base provides a

Unity Catalog Lakeguard: Industry-first and only data governance for multi-user Apache™ Spark clusters | Databricks Blog

Unlock the power of Apache Spark™ with Unity Catalog Lakeguard on Databricks Data Intelligence Platform. Run SQL, Python & Scala workloads with full data governance & cost-efficient multi-user compute.

Enhance PostgreSQL database security using hooks with Trusted Language Extensions | AWS Database Blog

PostgreSQL has the ability to authenticate user names and passwords using native credentials, though it lacks the ability to enforce specific password complexity and other advanced authentication policies without external identity services like LDAP or Kerberos. Trusted Language Extensions (TLE) for PostgreSQL (pg_tle), an open-source development kit used to build and package extensions, lets you […]

Improve Database Performance Testing with Custom SQL Scripts in pgbench

pgbench's default 'TPC-B like' workload may not reflect true performance. Use custom SQL scripts for more accurate tests. Demo with YugabyteDB.

How to Drop an Orphan LOB in Oracle Database – Make DBA Life Easy

In Oracle 11.2.0.3 database, after dropped some tables, there are still lob objects in dba_objects and dba_segments. Find the table name the lob belongs to: SQL> show parameter recyclebinNAME TYPE VALUE------------------------------------ ----------- -----------------------recyclebin string on SQL> select u.name, o.name TABLENAME, decode(bitand(c.property, 1), 1, ac.name, c.name) as column_name from sys.obj$ o, sys.col$ c, sys.attrcol$ ac,sys.lob$ l,sys.obj$…