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Netflix consolidates relational database infrastructure on Amazon Aurora, achieving up to 75% improved performance
Tech Optimizer
November 30, 2025

Netflix consolidates relational database infrastructure on Amazon Aurora, achieving up to 75% improved performance

Netflix operates a global streaming service using a distributed microservices architecture, with the Online Data Stores (ODS) team managing persistent data store solutions. The ODS team aims to enhance developer productivity and operational efficiency but faced challenges due to a fragmented relational database strategy involving multiple PostgreSQL-compatible engines, leading to operational inefficiencies and increased overhead for both infrastructure and application teams. To address these challenges, Netflix evaluated database solutions based on criteria focused on developer productivity, operational efficiency, performance reliability, and scalability. After thorough evaluation, Netflix selected Aurora PostgreSQL as the preferred database solution for relational workloads. Key reasons for choosing Aurora include its ability to handle single-Region and multi-Region workloads effectively, employing asynchronous storage-based replication for low-latency operations. Aurora's log-based write operations and shared storage architecture provide higher write throughput and lower latency. As a fully managed service, Aurora eliminates the manual operational overhead associated with self-managed databases, allowing developers to leverage existing PostgreSQL expertise with minimal code changes. Post-migration results for applications like Front50 and the Policy Engine showed significant improvements in latency and stability. Front50 experienced a 50% reduction in average latency and a 70% reduction in maximum latency. The Policy Engine noted overall latency improvements across all endpoints, with specific reductions in key functions, enhancing responsiveness and operational efficiency.
pgEdge Announces pgEdge Control Plane for Simplified Management and Orchestration of PostgreSQL Databases
Tech Optimizer
November 19, 2025

pgEdge Announces pgEdge Control Plane for Simplified Management and Orchestration of PostgreSQL Databases

pgEdge has launched the pgEdge Control Plane, a distributed application designed to manage and orchestrate PostgreSQL databases for single-region and globally distributed deployments. Key features include consistent database management through a declarative approach, high availability with read replicas and automated failover, seamless migration from single-region to distributed architectures, immutable infrastructure support, automated backup and restore capabilities, zero downtime maintenance, and multi-database host support. The Control Plane is compatible with pgEdge Enterprise Postgres and pgEdge Distributed Postgres, is fully open source, and supports deployment across major cloud providers and on-premises environments. It is included with paid pgEdge subscriptions, which offer 24/7 enterprise support.
EnterpriseDB Expands Postgres AI Platform to Boost Data Sovereignty and Accelerate AI Deployment
Tech Optimizer
October 27, 2025

EnterpriseDB Expands Postgres AI Platform to Boost Data Sovereignty and Accelerate AI Deployment

EnterpriseDB (EDB) has enhanced its EDB Postgres® AI (EDB PG AI) platform with new features to help enterprises unify data ecosystems, accelerate AI implementations, and transition from legacy systems. The platform focuses on data sovereignty, hybrid deployment adaptability, and operational efficiency in AI. Research indicates that companies prioritizing data and AI sovereignty can achieve ROI up to five times greater and are twice as likely to advance projects to full-scale production. The updated platform includes AI Pipelines for automated data preparation, enhancements to EDB PG AI Factory and Agent Studio for simplified model management, and upgraded migration tools with an AI Copilot for Oracle and Amazon Aurora migrations. These improvements aim to provide hybrid data sovereignty and avoid vendor lock-in while managing data across various infrastructures.
Google AI Studio Brings 'Vibe Coding': App Creation for Everyone
AppWizard
October 22, 2025

Google AI Studio Brings ‘Vibe Coding’: App Creation for Everyone

Google has updated its AI Studio platform with a new workflow called “vibe coding,” aimed at making AI application creation accessible to users with limited coding experience. The redesigned Build tab allows users to select AI models and modular features, articulate their application ideas in natural language, and generate application components using Gemini’s APIs. An interactive editor provides a code-assist chat interface and a code editor for various skill levels. Users can deploy their applications to Google Cloud Run with a single click. The platform includes an “I’m Feeling Lucky” button for generating randomized app concepts and offers context-aware feature suggestions through Gemini’s capabilities. This update positions Google as a competitor to other AI coding platforms and aims to reduce technical and financial barriers for users.
Supabase Secures $100M to Scale its PostgreSQL AI Database
Tech Optimizer
October 4, 2025

Supabase Secures $100M to Scale its PostgreSQL AI Database

Supabase has raised 0 million in a Series E funding round, valuing the company at billion. The funding aims to enhance its PostgreSQL AI database capabilities, particularly with the introduction of Multigres, an enterprise-scale solution. Supabase's advanced version of PostgreSQL integrates the open-source pgvector tool for efficient handling of AI model embeddings. The platform also features Edge Functions for serverless functions in JavaScript and collaborates with Hugging Face to process embeddings. Supabase's developer base has doubled from 2 million to over 4 million recently. The funding will support technological advancements, particularly in developing Multigres, led by Sugu Sougoumarane, co-creator of Vitess.
Microsoft Releases Windows ML: Simplifying AI Integration for Apps
Winsage
September 29, 2025

Microsoft Releases Windows ML: Simplifying AI Integration for Apps

Microsoft has launched Windows ML, an AI framework designed to integrate AI models into Windows applications, allowing local inferences on various hardware, including CPUs, GPUs, and NPUs. It abstracts hardware complexities to optimize performance across devices and supports AI formats like ONNX for compatibility with multiple vendors. Windows ML integrates with the Windows App SDK and offers APIs in languages such as C#, C++, and Python, facilitating the development of AI-enhanced applications. Performance benchmarks indicate up to 4x faster inference on NPUs compared to CPUs. The framework aims to create a hybrid AI ecosystem, balancing local and cloud processing. Windows 11’s 24H2 update requires NPU support in Copilot+ PCs, aligning with edge computing trends. Microsoft’s strategy emphasizes cross-hardware compatibility, potentially benefiting enterprise environments. Challenges remain in model training and security, and Microsoft plans to enhance Windows ML with future updates.
Streamline Structured + Unstructured Data Flows from Postgres with AI
Tech Optimizer
September 26, 2025

Streamline Structured + Unstructured Data Flows from Postgres with AI

A new software framework has emerged that aims to streamline application development across various platforms. Key features include cross-platform compatibility, an intuitive API, a modular architecture, and built-in tools for real-time collaboration. The framework is supported by a community of developers who contribute to its improvement, providing resources such as tutorials and documentation.
Building Video Game Recommender Systems with FastAPI, PostgreSQL, and Render: Part 1
Tech Optimizer
September 25, 2025

Building Video Game Recommender Systems with FastAPI, PostgreSQL, and Render: Part 1

The project involves creating a dynamic video game recommender system using PostgreSQL, FastAPI, and Render, which recommends games based on user interactions. It utilizes data from Steam's API, with a dataset of approximately 2000 games. The system architecture includes a PostgreSQL database and a FastAPI layer for data processing. Key components include a Game Table, User Table, User_Game Table, Game_Tags Table, and User_Recommendation Table. FastAPI is used to create APIs for data access and updates, while a recommendation pipeline generates suggestions based on user preferences using content-based filtering techniques. The project is divided into two parts: setup and theoretical foundations, and deployment. The system is deployed on Render for accessibility.
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