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Multi-tenant vector search with Amazon Aurora PostgreSQL and Amazon Bedrock Knowledge Bases

This article explores the fully managed approach using Amazon Bedrock Knowledge Bases to integrate data sources and generative AI applications with Amazon Aurora PostgreSQL.
It discusses a multi-tenant use case involving home survey data stored in an Amazon S3 bucket and the retrieval augmented generation (RAG) approach for response generation.
Amazon Bedrock Knowledge Bases simplifies the ingestion of data into an Aurora PostgreSQL-compatible vector store using a low-code approach.
Steps include configuring Aurora PostgreSQL for vector stores, ingesting data, and enforcing multi-tenant data isolation.
Querying vector data, leveraging RAG for enhanced prompts, and metadata filtering for tenant data isolation are highlighted.
Best practices for scaling, performance optimization, and deploying multi-tenant vector stores are outlined.
Fully-managed features like Amazon Bedrock streamline pipeline management for building efficient generative AI applications.
The article concludes by emphasizing the importance of tenant data isolation in a multi-tenant pooled data model.
Authors Josh Hart and Nihilson Gnanadason provide insights into building and modernizing SaaS applications on AWS.
Readers are encouraged to try both self-managed and fully managed approaches and share feedback for further development.
Recommendations for cleaning up resources to avoid additional charges and a call to action to explore the approaches are included.

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Amazon

Image Credit: Amazon

Self-managed multi-tenant vector search with Amazon Aurora PostgreSQL

Generative AI and ML tools are used to process unstructured data, making vector search essential for efficient data retrieval.
Amazon Aurora PostgreSQL-Compatible with the pgvector extension facilitates scalable vector store creation.
A multi-tenant design in SaaS applications requires tenant-aware data retrieval to ensure privacy and security.
The article discusses a self-managed approach for building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage.
Steps include creating a vector store, ingesting and retrieving vector data, and augmenting data for generative AI.
Key vector terminologies like vector embeddings, embedding models, and indexing methods are explained.
Row-level security and tenant isolation are enforced to prevent data breaches in a multi-tenant environment.
The self-managed approach involves using PostgreSQL for row-level security and ensures tenant-specific data retrieval.
The article provides code snippets for generating vector embeddings, inserting data into the vector store, and querying the vector database.
The self-managed approach gives flexibility but adds operational complexity, requiring considerations for performance and indexing strategies.

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Kotaku

428

Image Credit: Kotaku

The 29 Best Games For The Nintendo Switch

The article lists the best games for the Nintendo Switch, spanning various genres and playstyles.
Some of the highlighted games include The Legend of Zelda: Breath of the Wild, Super Smash Bros. Ultimate, and Hades.
Other notable titles featured are Super Mario Odyssey, Animal Crossing: New Horizons, and Fire Emblem: Three Houses.
The list has been updated over the years, with additions like Splatoon 3 and Pokémon Legends: Arceus.
Games removed through updates include Splatoon 2, Into the Breach, and Golf Story.
Each game is briefly described, highlighting its unique gameplay and appeal to different types of players.
The games range from action-packed adventures like Xenoblade Chronicles 3 to charming life simulations like Stardew Valley.
Overall, the article serves as a comprehensive guide to top games available on the Nintendo Switch.

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Alvinashcraft

Image Credit: Alvinashcraft

Dew Drop – February 20, 2025 (#4366)

Microsoft announced the Chroma DB C# SDK and Majorana 1 quantum processor.
.NET Rocks! featured Uno Hot Design and GitHub Copilot enhancements.
Updates in Deno 2.2, AWS Documentation, and Rust Beta SDK for Azure were highlighted.
New MAUI Bullet Chart for product sales data tracking was introduced.
MSVC C++ Code Analysis updates and AutoMapper 14.0 release were discussed.
Arm Extension for GitHub Copilot and Azure AI Foundry Labs were unveiled.
The article covers various topics like adaptive vs. responsive design and AI agent building.
Xcode IDE update, TypeScript enums in React Native guide, and Flutter Navigation topic were featured.
Screencasts on WinForms projects and Majorana 1 explanation were showcased.
Podcasts on tech topics like Nuxt, quantum computing, and Scrum strategies were shared.

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Towards Data Science

272

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Advanced Time Intelligence in DAX with Performance in Mind

The article discusses advanced Time Intelligence in DAX with a focus on efficiency and performance.
It covers scenarios not easily handled by standard Time Intelligence functions and uses an extended date table for support.
Examples include aligning Fiscal Year with the calendar year for calculations like Year-to-Date (YTD).
The article demonstrates working with the ContosoRetailDW data model and focusing on enhancing DAX performance.
Utilizing DAX-Studio for performance analysis and emphasizing server timing for efficiency evaluation.
The article details calculating rolling sums over past periods and addressing challenges like leap years in DAX calculations.
It showcases methods for handling weekly analysis and building custom weekly measures like Week-to-Date (WTD).
The article also delves into handling Year-to-Date (YTD) calculations starting from the first day of the first week of the Fiscal year.
Emphasis is placed on creating an extended date table to simplify DAX calculations and improve performance.
Various approaches and DAX codes for daily and weekly YTD calculations are provided, highlighting efficiency considerations.

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Dbi-Services

171

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SUMA (SUSE Manager) is back and it has a new name: SUSE Multi Linux

SUSE Multi Linux Manager, previously known as SUSE Manager (SUMA), is an infrastructure management solution for Linux distributions.
It simplifies and secures the management of various Linux distributions with a web console.
SUSE has introduced new repositories for historic distributions like CentOS 6 and RHEL 5.
Key features include automated patching, content lifecycle management, real-time monitoring, automation, and security tools.
Version 5.0 offers containerized deployment for resilience and simplified recovery.
Installation involves downloading the server, registration, and adding necessary repositories.
SUSE Multi Linux allows automated OpenSCAP scans, patch management, and remote commands for server management.
It supports a wide range of Linux systems, provides automatic updates, update testing, security checks, and container usage.
The platform is easy to get started with but requires substantial storage for Linux repositories.
In conclusion, SUSE Multi Linux Manager is a comprehensive solution for managing diverse Linux systems efficiently.

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Infoq

316

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Google Releases Spanner Graph into General Availability

Google Cloud has released Spanner Graph into general availability, offering new capabilities
Spanner Graph builds on Cloud Spanner, providing high availability and scalability
The new capabilities with the GA release include a Spanner Graph Notebook, GraphRAG integration, Graph schema in Spanner Studio, and support for path data type and functions
Spanner Graph is designed for real-time analysis of large-scale graph data for applications like fraud detection and recommendation engines

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Dev

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The Best AI-Powered SQL Optimization Tools in 2025

SQL optimization is crucial for improving performance and reducing the load on the database server but is a challenging task that traditionally involves manual analysis.
The trend of integrating AI into SQL optimization tools has emerged to automate query analysis, generate optimized plans, and offer time-saving benefits with better accuracy.
Five notable AI-powered SQL optimization tools in 2025 include EverSQL, PawSQL, Tosska DB Ace Enterprise, SQL Tuning, and SQLFlash, each catering to different needs and database environments.
EverSQL utilizes machine learning for deep query analysis, PawSQL is known for its user-friendly interface, Tosska DB Ace Enterprise is comprehensive for large-scale applications, SQL Tuning is an online tool for quick improvements, and SQLFlash is beginner-friendly with advanced performance optimization.
SQLFlash stands out for supporting all relational databases, automatically identifying bottlenecks, and offering high-performance results without complex optimization techniques.
Comparison of these tools reveals differences in multi-database support, dynamic SQL optimization, execution plan visualization, index recommendation, optimization transparency, and performance quantification dimensions.
The use of AI-powered SQL optimization tools is essential in 2025, driving automation and efficiency in query optimization processes and catering to various user needs.
AI integration in SQL optimization tools signifies innovation and effectiveness in managing complex data environments, with each tool providing unique strengths and functionalities.
As the field evolves, AI technology will continue to enhance SQL optimization, offering advanced solutions for database performance and data management.
The future of SQL optimization lies in leveraging AI capabilities, including machine learning algorithms, user-friendly interfaces, and comprehensive optimization features for enhanced efficiency.

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Inside

343

JavaOne 2025 Features Some of the Best Java Experts From Around the World

JavaOne 2025 features some of the best Java experts from around the world.
The list of featured speakers includes Java luminaries from Oracle's Java team and influencers from the wider Java community.
Passionate about imparting their knowledge, the speakers aim to inspire innovation and share insights on the latest Java features, Virtual Threads, and AI.
JavaOne 2025 offers breakout sessions to facilitate learning, sharing, and collaboration, with an emphasis on energizing and inspiring attendees.

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20 Likes

Medium

198

SQL Performance Tuning Scenario #3: Indexing Based on Search Patterns (SARGability)

SARGability refers to queries where the search predicates in the WHERE clause are written in a way that allows efficient use of indexes.
Using SARGable queries allows for index seek instead of full table scans, resulting in faster and more efficient query execution.
Writing SARGable queries is important for optimal index utilization and query performance.
By avoiding functions on indexed columns and following best practices, effective use of indexes can be maintained as data grows.

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Medium

375

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Setting Up a PostgreSQL Database Using GCP Cloud SQL

This guide explains how to set up a PostgreSQL database using GCP Cloud SQL.
It covers the process of creating a PostgreSQL instance, applying performance configurations, implementing security measures, setting up backups, and monitoring the database.
By following the steps, users will have a fully functional PostgreSQL environment on GCP for testing, development, or small-scale production scenarios.
The guide also emphasizes the benefits of using PostgreSQL in terms of learning best practices that can be applied to other databases.

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Dbi-Services

122

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How to: Deploying a virtual machine using Infomaniak Public Cloud

To deploy a virtual machine in Infomaniak Public Cloud, go to Instances and click on "Launch Instance".
Select the parameters for your virtual machine like project, name, and availability zone.
Choose the OS and disk size in the Source tab, and select the shape (Flavor) of the virtual machine.
Configure optional parameters such as network ports, security groups, key pair, script, server groups, scheduler hints, and metadata.

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Dev

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How to Get the Query Builder to Output Its Raw SQL Query as a String

Understanding the raw SQL query can be beneficial in several cases: Debugging, Logging, Optimization.
Laravel provides a convenient method to retrieve the SQL query with bindings.
The toSql() method returns the raw SQL query as a string without executing it.
There are multiple ways to retrieve the raw SQL query in Laravel, such as using toSql() method, manually replacing bindings, logging queries using query listeners, and using Laravel Debugbar.

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Dev

334

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Do Ordered Grouping and Aggregation within Groups — From SQL to SPL #1

A certain database table describes the payment cycle for multiple projects.
The task is to identify each payment month for each project and calculate the total amount for that payment cycle.
SQL does not have a direct ordered grouping, it requires the use of window functions and subqueries.
SPL (Structured Query Language Processing Language) supports convenient ordered calculations and offers straightforward code.

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Mysql

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The Potential of Query Attributes in MySQL

Query Attributes in MySQL are a new feature introduced in MySQL Protocol available since version 8.0.23.
They offer per-query metadata in the form of key/value pairs, in contrast to Connection Attributes which provide per-connection metadata.
Connection attributes are set by connectors or applications, aiding in understanding what connects to the server, but have limitations in certain scenarios.
Query Attributes, when the CLIENT_QUERY_ATTRIBUTES flag is set, allow setting key/value pairs on a per-query basis, distinct from connection attributes.
Changes in network packets for COM_STMT_EXECUTE and COM_QUERY commands enable sending parameters and parameter names with queries.
MySQL C API introduces functions like mysql_bind_param() and mysql_stmt_bind_named_param() to send query attributes with regular and prepared statements.
MySQL Connectors like MySQL Connector/Python and MySQL Connector/J have added functions to support query attributes.
MySQL Client now allows setting query attributes using 'query_attributes ' to enhance query metadata.
Query Attributes can be used for sending metadata like Trace ID, user info, page name, etc., improving trace linking, proxying, or web page rendering.
Despite the benefits, query attributes still have limitations, including not being included in binlogs, general log, or slow query log.

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