#and PostgreSQL?

[lucid-outsourcing-solutions]

How to Configure ColdFusion Datasource for MySQL, SQL Server, and PostgreSQL?

[nixcraft]

Wait what... ? this is dangerous knowledge.

[dire-straits-fn8ic]

a beautiful baby girl named PostgreSQL

[xpertbytes]

Expert React & Laravel Solutions for Modern Businesses!

[robomad]

Deploying Django Applications to Heroku

Learn how to deploy your Django applications to Heroku with this comprehensive guide. Follow step-by-step instructions to set up, configure, and deploy your app seamlessly.

Introduction Deploying Django applications to Heroku is a streamlined process thanks to Heroku’s powerful platform-as-a-service (PaaS) offerings. Heroku abstracts away much of the infrastructure management, allowing developers to focus on building and deploying their applications. This guide will walk you through the steps to deploy a Django application to Heroku, including setting up the…

[technicontrastron]

Looking at data 👍

[briskwinits]

With SQL Server, Oracle MySQL, MongoDB, and PostgreSQL and more, we are your dedicated partner in managing, optimizing, securing, and supporting your data infrastructure.

For more, visit: https://briskwinit.com/database-services/

[chelnik]

Heading back for another day of LinuxFest Northwest!

[teamfemmebabe]

Hey Friends - what's good (via A woman with a black and white wig. - Glam Girl X)

[perimattic]

Looking for a trusted PostgreSQL development company? We offer expert PostgreSQL development services tailored to your business needs—whether it’s custom solutions, database migration, or performance tuning. Scale confidently with our reliable, secure, and high-performing PostgreSQL solutions.

[govindhtech]

How AlloyDB AI Query Engine Empower Smart Apps Developers

AlloyDB AI query engine helps developers build smarter apps with quick, intelligent data management and rich insights.

Artificial intelligence and intelligent agents, which can understand commands and queries in natural language and act on their own, are causing major changes. The “AI-ready” enterprise database, a dynamic, intelligent engine that understands the semantics of both structured and unstructured data and leverages foundation models to build a platform that opens up new enterprise data possibilities, is at the heart of this transformation.

Google Cloud Next introduced many new AlloyDB AI technologies this week to speed up intelligent agent and application development. These include autonomous vector index management, high-performance filtered vector search with enhanced semantic search, and improved search quality using the recently announced Vertex AI Ranking API and AlloyDB AI query engine. Also, the AI query engine filters SQL queries with AI-powered operators.

They are adding natural language capabilities to provide people and bots deep insights from natural language searches. These advancements make AlloyDB the foundation of agentic AI, converting it from a database for storing data and conducting SQL queries to one where intelligent agents may interact with the data and conduct autonomous research.

Effective, high-quality, and simple semantic search

Modern apps need smart data retrieval that combines structured and unstructured text and graphics. AlloyDB AI enabled semantic searches over unstructured data and extensively integrated vector search with PostgreSQL to keep search results updated. Google cloud next AlloyDB AI features address customer needs for better search results, faster performance, and cheap autonomous maintenance.

Adaptive filtering, a new mechanism in preview, ensures filters, joins, and vector indexes function effectively together. After learning the genuine filter selectivity as it accesses data, adaptive filtering optimises the query strategy and switches amongst filtered vector search methods.

Vector index auto-maintenance: reduces vector index rebuilds and ensures correctness and performance even when data changes. Vector index auto-maintenance can be enabled while building or editing an index.

The recently unveiled AlloyDB AI query engine may enhance semantic search by combining vector search with high-accuracy AI reranking utilising the new Vertex AI cross-attention Ranking API. After vector search generates initial candidates (like Top N), reranking capability uses the high-quality cross-attention Ranking API to dependably identify the best results (like Top 10). AlloyDB AI can integrate with any third-party ranking API, including bespoke ones, for maximum versatility.

Remember evaluator. This widely available feature provides transparency to manage and improve vector search results. A simple stored procedure may evaluate end-to-end recall for any query, even complex ones like filters, joins, and reranking.

Previously many times that amount, index build parallelisation is now commonly accessible and allows developers to produce 1 billion-row indexes in hours. AlloyDB AI launches parallel processes to distribute the load and build indexes faster.

The deep integration of AlloyDB AI's Scalable Nearest Neighbours (ScaNN) vector index with PostgreSQL's query planner speeds up performance:

10 times quicker filtered vector search than PostgreSQL's HNSW index.

Index building takes ten times less time than PostgreSQL's HNSW index.

Vector search is four times quicker than PostgreSQL's HNSW index.

AI AlloyDB natural language

AI technologies helped natural language interfaces on databases improve in 2024 by converting agent and user requests into SQL queries that give results.

Additional precision requires a quantum leap. Its new capabilities allow you to design interactive natural language user interfaces that effectively comprehend user intent and produce exceptionally accurate mappings from user questions to SQL queries that offer replies, improving on last year's natural language support.

Disambiguation: Natural language is ambiguous. AlloyDB AI natural language interface will ask follow-up questions to gather further user intent data. The database is excellent at resolving ambiguity since it's often embedded in the data.

If a query mentions “John Smith,” the database may include two John Smiths or a “Jon Smith” with a misspelt initial. AlloyDB concept categories and the values index help find relevant entities and ideas when the inquiry is unclear.

High accuracy and intent explanation: AlloyDB AI natural language uses faceted and plain templates that match parameterised SQL queries to answer important and predictable enquiries with almost verified correctness.

The millions of product attributes on a retailer's product search page might overwhelm a screen-based faceted search experience. But even with one simple search field, a faceted search template can handle any query that directly or indirectly raises any combination of property criteria. Additional templates can be provided to expand query coverage beyond those AlloyDB generates from query logs. AlloyDB clearly explains how it understands user queries to ensure results reliability.

In unpredictable questions that require flexible responses, AlloyDB automatically adds rich data from the schema, data (including sample data), and query logs to the context used to map the question to SQL.

Parameterised secure views: AlloyDB's new parameterised secure views restrict database-level access to end-user data to prevent quick injection attacks.

In addition to AlloyDB, Google Agentspace offers AlloyDB AI natural language for creating agents that can reply to questions by combining AlloyDB data with other databases or the internet.

AlloyDB AI query engine

The AlloyDB AI query engine can extract deep semantic insights from corporate data using AI-powered SQL operators, allowing user-friendly and powerful AI applications. AI query engines employ Model Endpoint Management to call any AI model on any platform.

The AlloyDB AI query engine and new AI model-enabled capabilities will be examined:

Artificial intelligence query engine AlloyDB SQL now supports simple but useful AI operators like AI.IF() for filters and joins and AI.RANK() for ordering. These operators use plain language to communicate SQL query ranking and filtering criteria. Cross-attention models, which are strong because of foundation models and real-world information, may bring logic and practical knowledge to SQL queries. For the most relevant results, AI.RANK() can use the Vertex AI Ranking API.

Previous versions of AlloyDB AI made multimodal embeddings from SQL statement text easy for SQL developers. It has expanded this functionality to integrate text, photographs, and videos to provide multimodal search.

Updated text embedding generation: AlloyDB AI query engine integrates Google DeepMind text-embedding creation out of the box.

Beginning

The AlloyDB AI query engine, next-generation natural language support, and better filtered vector search unveiled today provide the framework for future databases, according to Google cloud. AI-ready data gives agents proactive insights to anticipate and act. AlloyDB AI's database revolution will let you boldly join this intelligent future and unlock your data's boundless potential.

[mydbops]

​The Mydbops Blog offers expert insights and practical guidance on managing open-source databases such as MySQL, MariaDB, MongoDB, PostgreSQL, TiDB, and Cassandra. It covers topics like performance optimization, security hardening, replication strategies, and the latest trends in database technology. The blog serves as a valuable resource for database administrators and developers seeking to enhance their knowledge and stay updated with industry best practices. ​

[simple-logic]

#QuizTime Which database is known for its scalability and high availability?

A) Oracle 📊 B) MySQL 🛢️ C) PostgreSQL 🐘 D) All of the above ✅

Comments your answer below👇

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[blogisy]

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